id	sid	tid	token	lemma	pos
ajst-20775	1	1	academic	academic	ADJ
ajst-20775	1	2	journal	journal	NOUN
ajst-20775	1	3	of	of	ADP
ajst-20775	1	4	science	science	NOUN
ajst-20775	1	5	and	and	CCONJ
ajst-20775	1	6	technology	technology	NOUN
ajst-20775	1	7	issn	issn	NOUN
ajst-20775	1	8	:	:	PUNCT
ajst-20775	1	9	2771	2771	NUM
ajst-20775	1	10	-	-	SYM
ajst-20775	1	11	3032	3032	NUM
ajst-20775	1	12	|	|	NOUN
ajst-20775	1	13	vol	vol	NOUN
ajst-20775	1	14	.	.	PROPN
ajst-20775	2	1	10	10	NUM
ajst-20775	2	2	,	,	PUNCT
ajst-20775	2	3	no	no	INTJ
ajst-20775	2	4	.	.	NOUN
ajst-20775	2	5	3	3	NUM
ajst-20775	2	6	,	,	PUNCT
ajst-20775	2	7	2024	2024	NUM
ajst-20775	2	8	37	37	NUM
ajst-20775	2	9	fatigue	fatigue	NOUN
ajst-20775	2	10	driving	drive	VERB
ajst-20775	2	11	detection	detection	NOUN
ajst-20775	2	12	based	base	VERB
ajst-20775	2	13	on	on	ADP
ajst-20775	2	14	driver	driver	NOUN
ajst-20775	2	15	facial	facial	ADJ
ajst-20775	2	16	temporal	temporal	ADJ
ajst-20775	2	17	sequences	sequence	NOUN
ajst-20775	2	18	yonglin	yonglin	PROPN
ajst-20775	2	19	qian	qian	PROPN
ajst-20775	2	20	,	,	PUNCT
ajst-20775	2	21	guoqiang	guoqiang	PROPN
ajst-20775	2	22	zheng	zheng	PROPN
ajst-20775	2	23	,	,	PUNCT
ajst-20775	2	24	yifan	yifan	PROPN
ajst-20775	2	25	xie	xie	PROPN
ajst-20775	2	26	,	,	PUNCT
ajst-20775	2	27	xiangshuai	xiangshuai	PROPN
ajst-20775	2	28	lv	lv	PROPN
ajst-20775	2	29	and	and	CCONJ
ajst-20775	2	30	weizhen	weizhen	PROPN
ajst-20775	2	31	zhang	zhang	PROPN
ajst-20775	2	32	school	school	PROPN
ajst-20775	2	33	of	of	ADP
ajst-20775	2	34	information	information	NOUN
ajst-20775	2	35	engineering	engineering	PROPN
ajst-20775	2	36	,	,	PUNCT
ajst-20775	2	37	henan	henan	PROPN
ajst-20775	2	38	university	university	PROPN
ajst-20775	2	39	of	of	ADP
ajst-20775	2	40	science	science	NOUN
ajst-20775	2	41	and	and	CCONJ
ajst-20775	2	42	technology	technology	NOUN
ajst-20775	2	43	,	,	PUNCT
ajst-20775	2	44	luoyang	luoyang	PROPN
ajst-20775	2	45	471023	471023	NUM
ajst-20775	2	46	,	,	PUNCT
ajst-20775	2	47	china	china	PROPN
ajst-20775	2	48	abstract	abstract	NOUN
ajst-20775	2	49	:	:	PUNCT
ajst-20775	2	50	addressing	address	VERB
ajst-20775	2	51	the	the	DET
ajst-20775	2	52	issues	issue	NOUN
ajst-20775	2	53	of	of	ADP
ajst-20775	2	54	low	low	ADJ
ajst-20775	2	55	real	real	ADJ
ajst-20775	2	56	-	-	PUNCT
ajst-20775	2	57	time	time	NOUN
ajst-20775	2	58	performance	performance	NOUN
ajst-20775	2	59	and	and	CCONJ
ajst-20775	2	60	high	high	ADJ
ajst-20775	2	61	false	false	ADJ
ajst-20775	2	62	positive	positive	ADJ
ajst-20775	2	63	rates	rate	NOUN
ajst-20775	2	64	in	in	ADP
ajst-20775	2	65	driver	driver	NOUN
ajst-20775	2	66	fatigue	fatigue	NOUN
ajst-20775	2	67	detection	detection	NOUN
ajst-20775	2	68	methods	method	NOUN
ajst-20775	2	69	based	base	VERB
ajst-20775	2	70	on	on	ADP
ajst-20775	2	71	deep	deep	ADJ
ajst-20775	2	72	learning	learning	NOUN
ajst-20775	2	73	,	,	PUNCT
ajst-20775	2	74	this	this	DET
ajst-20775	2	75	paper	paper	NOUN
ajst-20775	2	76	proposes	propose	VERB
ajst-20775	2	77	a	a	DET
ajst-20775	2	78	temporal	temporal	ADJ
ajst-20775	2	79	sequence	sequence	NOUN
ajst-20775	2	80	transformer	transformer	NOUN
ajst-20775	2	81	-	-	PUNCT
ajst-20775	2	82	based	base	VERB
ajst-20775	2	83	fatigue	fatigue	NOUN
ajst-20775	2	84	detection	detection	NOUN
ajst-20775	2	85	method	method	NOUN
ajst-20775	2	86	grounded	ground	VERB
ajst-20775	2	87	in	in	ADP
ajst-20775	2	88	the	the	DET
ajst-20775	2	89	localization	localization	NOUN
ajst-20775	2	90	of	of	ADP
ajst-20775	2	91	facial	facial	ADJ
ajst-20775	2	92	landmarks	landmark	NOUN
ajst-20775	2	93	in	in	ADP
ajst-20775	2	94	drivers	driver	NOUN
ajst-20775	2	95	.	.	PUNCT
ajst-20775	3	1	initially	initially	ADV
ajst-20775	3	2	,	,	PUNCT
ajst-20775	3	3	the	the	DET
ajst-20775	3	4	facial	facial	ADJ
ajst-20775	3	5	positions	position	NOUN
ajst-20775	3	6	are	be	AUX
ajst-20775	3	7	obtained	obtain	VERB
ajst-20775	3	8	using	use	VERB
ajst-20775	3	9	the	the	DET
ajst-20775	3	10	single	single	ADJ
ajst-20775	3	11	-	-	PUNCT
ajst-20775	3	12	stage	stage	NOUN
ajst-20775	3	13	face	face	NOUN
ajst-20775	3	14	detection	detection	NOUN
ajst-20775	3	15	algorithm	algorithm	NOUN
ajst-20775	3	16	retinaface	retinaface	NOUN
ajst-20775	3	17	.	.	PUNCT
ajst-20775	4	1	subsequently	subsequently	ADV
ajst-20775	4	2	,	,	PUNCT
ajst-20775	4	3	a	a	DET
ajst-20775	4	4	lightweight	lightweight	ADJ
ajst-20775	4	5	gm	gm	PROPN
ajst-20775	4	6	module	module	NOUN
ajst-20775	4	7	is	be	AUX
ajst-20775	4	8	designed	design	VERB
ajst-20775	4	9	as	as	ADP
ajst-20775	4	10	the	the	DET
ajst-20775	4	11	principal	principal	ADJ
ajst-20775	4	12	feature	feature	NOUN
ajst-20775	4	13	extraction	extraction	NOUN
ajst-20775	4	14	module	module	NOUN
ajst-20775	4	15	for	for	ADP
ajst-20775	4	16	constructing	construct	VERB
ajst-20775	4	17	a	a	DET
ajst-20775	4	18	multi	multi	ADJ
ajst-20775	4	19	-	-	ADJ
ajst-20775	4	20	scale	scale	ADJ
ajst-20775	4	21	fusion	fusion	NOUN
ajst-20775	4	22	facial	facial	ADJ
ajst-20775	4	23	landmark	landmark	NOUN
ajst-20775	4	24	detection	detection	NOUN
ajst-20775	4	25	network	network	NOUN
ajst-20775	4	26	,	,	PUNCT
ajst-20775	4	27	and	and	CCONJ
ajst-20775	4	28	facial	facial	ADJ
ajst-20775	4	29	fatigue	fatigue	NOUN
ajst-20775	4	30	feature	feature	NOUN
ajst-20775	4	31	parameters	parameter	NOUN
ajst-20775	4	32	based	base	VERB
ajst-20775	4	33	on	on	ADP
ajst-20775	4	34	temporal	temporal	ADJ
ajst-20775	4	35	sequences	sequence	NOUN
ajst-20775	4	36	are	be	AUX
ajst-20775	4	37	calculated	calculate	VERB
ajst-20775	4	38	according	accord	VERB
ajst-20775	4	39	to	to	ADP
ajst-20775	4	40	the	the	DET
ajst-20775	4	41	facial	facial	ADJ
ajst-20775	4	42	landmarks	landmark	NOUN
ajst-20775	4	43	.	.	PUNCT
ajst-20775	5	1	finally	finally	ADV
ajst-20775	5	2	,	,	PUNCT
ajst-20775	5	3	a	a	DET
ajst-20775	5	4	fatigue	fatigue	NOUN
ajst-20775	5	5	driving	drive	VERB
ajst-20775	5	6	classification	classification	NOUN
ajst-20775	5	7	method	method	NOUN
ajst-20775	5	8	based	base	VERB
ajst-20775	5	9	on	on	ADP
ajst-20775	5	10	temporal	temporal	ADJ
ajst-20775	5	11	sequences	sequence	NOUN
ajst-20775	5	12	transformers	transformer	NOUN
ajst-20775	5	13	is	be	AUX
ajst-20775	5	14	developed	develop	VERB
ajst-20775	5	15	for	for	ADP
ajst-20775	5	16	classifying	classify	VERB
ajst-20775	5	17	the	the	DET
ajst-20775	5	18	sequences	sequence	NOUN
ajst-20775	5	19	of	of	ADP
ajst-20775	5	20	fatigue	fatigue	NOUN
ajst-20775	5	21	feature	feature	NOUN
ajst-20775	5	22	parameters	parameter	NOUN
ajst-20775	5	23	.	.	PUNCT
ajst-20775	6	1	experimental	experimental	ADJ
ajst-20775	6	2	results	result	NOUN
ajst-20775	6	3	demonstrate	demonstrate	VERB
ajst-20775	6	4	that	that	SCONJ
ajst-20775	6	5	the	the	DET
ajst-20775	6	6	inference	inference	NOUN
ajst-20775	6	7	time	time	NOUN
ajst-20775	6	8	required	require	VERB
ajst-20775	6	9	for	for	ADP
ajst-20775	6	10	facial	facial	ADJ
ajst-20775	6	11	detection	detection	NOUN
ajst-20775	6	12	and	and	CCONJ
ajst-20775	6	13	landmark	landmark	NOUN
ajst-20775	6	14	detection	detection	NOUN
ajst-20775	6	15	is	be	AUX
ajst-20775	6	16	merely	merely	ADV
ajst-20775	6	17	16.8	16.8	NUM
ajst-20775	6	18	milliseconds	millisecond	NOUN
ajst-20775	6	19	,	,	PUNCT
ajst-20775	6	20	with	with	ADP
ajst-20775	6	21	the	the	DET
ajst-20775	6	22	per	per	ADP
ajst-20775	6	23	-	-	PUNCT
ajst-20775	6	24	frame	frame	NOUN
ajst-20775	6	25	inference	inference	NOUN
ajst-20775	6	26	time	time	NOUN
ajst-20775	6	27	for	for	ADP
ajst-20775	6	28	facial	facial	ADJ
ajst-20775	6	29	landmark	landmark	NOUN
ajst-20775	6	30	detection	detection	NOUN
ajst-20775	6	31	being	be	AUX
ajst-20775	6	32	only	only	ADV
ajst-20775	6	33	2.5	2.5	NUM
ajst-20775	6	34	milliseconds	millisecond	NOUN
ajst-20775	6	35	,	,	PUNCT
ajst-20775	6	36	thus	thus	ADV
ajst-20775	6	37	fulfilling	fulfil	VERB
ajst-20775	6	38	the	the	DET
ajst-20775	6	39	real	real	ADJ
ajst-20775	6	40	-	-	PUNCT
ajst-20775	6	41	time	time	NOUN
ajst-20775	6	42	requirements	requirement	NOUN
ajst-20775	6	43	of	of	ADP
ajst-20775	6	44	fatigue	fatigue	NOUN
ajst-20775	6	45	driving	drive	VERB
ajst-20775	6	46	detection	detection	NOUN
ajst-20775	6	47	during	during	ADP
ajst-20775	6	48	the	the	DET
ajst-20775	6	49	feature	feature	NOUN
ajst-20775	6	50	extraction	extraction	NOUN
ajst-20775	6	51	phase	phase	NOUN
ajst-20775	6	52	.	.	PUNCT
ajst-20775	7	1	a	a	DET
ajst-20775	7	2	temporal	temporal	ADJ
ajst-20775	7	3	sequence	sequence	NOUN
ajst-20775	7	4	of	of	ADP
ajst-20775	7	5	fatigue	fatigue	NOUN
ajst-20775	7	6	feature	feature	NOUN
ajst-20775	7	7	parameters	parameter	NOUN
ajst-20775	7	8	built	build	VERB
ajst-20775	7	9	on	on	ADP
ajst-20775	7	10	the	the	DET
ajst-20775	7	11	nthu	nthu	ADJ
ajst-20775	7	12	-	-	PUNCT
ajst-20775	7	13	ddd	ddd	NOUN
ajst-20775	7	14	dataset	dataset	NOUN
ajst-20775	7	15	and	and	CCONJ
ajst-20775	7	16	the	the	DET
ajst-20775	7	17	trained	train	VERB
ajst-20775	7	18	temporal	temporal	ADJ
ajst-20775	7	19	sequence	sequence	NOUN
ajst-20775	7	20	transformer	transformer	NOUN
ajst-20775	7	21	model	model	NOUN
ajst-20775	7	22	resulted	result	VERB
ajst-20775	7	23	in	in	ADP
ajst-20775	7	24	an	an	DET
ajst-20775	7	25	accuracy	accuracy	NOUN
ajst-20775	7	26	rate	rate	NOUN
ajst-20775	7	27	of	of	ADP
ajst-20775	7	28	91.4	91.4	NUM
ajst-20775	7	29	%	%	NOUN
ajst-20775	7	30	for	for	ADP
ajst-20775	7	31	the	the	DET
ajst-20775	7	32	proposed	propose	VERB
ajst-20775	7	33	method	method	NOUN
ajst-20775	7	34	.	.	PUNCT
ajst-20775	8	1	keywords	keyword	NOUN
ajst-20775	8	2	:	:	PUNCT
ajst-20775	8	3	fatigue	fatigue	NOUN
ajst-20775	8	4	driving	drive	VERB
ajst-20775	8	5	detection	detection	NOUN
ajst-20775	8	6	;	;	PUNCT
ajst-20775	8	7	facial	facial	ADJ
ajst-20775	8	8	landmark	landmark	NOUN
ajst-20775	8	9	detection	detection	NOUN
ajst-20775	8	10	;	;	PUNCT
ajst-20775	8	11	transformer	transformer	NOUN
ajst-20775	8	12	.	.	PUNCT
ajst-20775	9	1	1	1	X
ajst-20775	9	2	.	.	X
ajst-20775	9	3	introduction	introduction	NOUN
ajst-20775	9	4	according	accord	VERB
ajst-20775	9	5	to	to	ADP
ajst-20775	9	6	estimates	estimate	NOUN
ajst-20775	9	7	by	by	ADP
ajst-20775	9	8	the	the	DET
ajst-20775	9	9	national	national	PROPN
ajst-20775	9	10	highway	highway	PROPN
ajst-20775	9	11	traffic	traffic	PROPN
ajst-20775	9	12	safety	safety	PROPN
ajst-20775	9	13	administration	administration	PROPN
ajst-20775	9	14	,	,	PUNCT
ajst-20775	9	15	fatigue	fatigue	NOUN
ajst-20775	9	16	driving	driving	NOUN
ajst-20775	9	17	contributes	contribute	VERB
ajst-20775	9	18	to	to	ADP
ajst-20775	9	19	100,000	100,000	NUM
ajst-20775	9	20	traffic	traffic	NOUN
ajst-20775	9	21	accidents	accident	NOUN
ajst-20775	9	22	annually	annually	ADV
ajst-20775	9	23	,	,	PUNCT
ajst-20775	9	24	resulting	result	VERB
ajst-20775	9	25	in	in	ADP
ajst-20775	9	26	over	over	ADP
ajst-20775	9	27	1,550	1,550	NUM
ajst-20775	9	28	deaths	death	NOUN
ajst-20775	9	29	,	,	PUNCT
ajst-20775	9	30	71,000	71,000	NUM
ajst-20775	9	31	injuries	injury	NOUN
ajst-20775	9	32	,	,	PUNCT
ajst-20775	9	33	and	and	CCONJ
ajst-20775	9	34	a	a	DET
ajst-20775	9	35	loss	loss	NOUN
ajst-20775	9	36	of	of	ADP
ajst-20775	9	37	$	$	SYM
ajst-20775	9	38	12.5	12.5	NUM
ajst-20775	9	39	billion[1	billion[1	PROPN
ajst-20775	9	40	]	]	PUNCT
ajst-20775	9	41	.	.	PUNCT
ajst-20775	10	1	therefore	therefore	ADV
ajst-20775	10	2	,	,	PUNCT
ajst-20775	10	3	realtime	realtime	ADJ
ajst-20775	10	4	detection	detection	NOUN
ajst-20775	10	5	of	of	ADP
ajst-20775	10	6	a	a	DET
ajst-20775	10	7	driver	driver	NOUN
ajst-20775	10	8	's	's	PART
ajst-20775	10	9	state	state	NOUN
ajst-20775	10	10	during	during	ADP
ajst-20775	10	11	driving	drive	VERB
ajst-20775	10	12	and	and	CCONJ
ajst-20775	10	13	timely	timely	ADJ
ajst-20775	10	14	alerts	alert	NOUN
ajst-20775	10	15	when	when	SCONJ
ajst-20775	10	16	the	the	DET
ajst-20775	10	17	driver	driver	NOUN
ajst-20775	10	18	is	be	AUX
ajst-20775	10	19	fatigued	fatigued	ADJ
ajst-20775	10	20	or	or	CCONJ
ajst-20775	10	21	drowsy	drowsy	NOUN
ajst-20775	10	22	are	be	AUX
ajst-20775	10	23	crucial	crucial	ADJ
ajst-20775	10	24	for	for	ADP
ajst-20775	10	25	reducing	reduce	VERB
ajst-20775	10	26	traffic	traffic	NOUN
ajst-20775	10	27	accidents	accident	NOUN
ajst-20775	10	28	caused	cause	VERB
ajst-20775	10	29	by	by	ADP
ajst-20775	10	30	fatigue	fatigue	NOUN
ajst-20775	10	31	driving	driving	NOUN
ajst-20775	10	32	.	.	PUNCT
ajst-20775	11	1	currently	currently	ADV
ajst-20775	11	2	,	,	PUNCT
ajst-20775	11	3	fatigue	fatigue	NOUN
ajst-20775	11	4	driving	drive	VERB
ajst-20775	11	5	detection	detection	NOUN
ajst-20775	11	6	methods	method	NOUN
ajst-20775	11	7	can	can	AUX
ajst-20775	11	8	be	be	AUX
ajst-20775	11	9	mainly	mainly	ADV
ajst-20775	11	10	classified	classify	VERB
ajst-20775	11	11	into	into	ADP
ajst-20775	11	12	three	three	NUM
ajst-20775	11	13	categories	category	NOUN
ajst-20775	11	14	based	base	VERB
ajst-20775	11	15	on	on	ADP
ajst-20775	11	16	the	the	DET
ajst-20775	11	17	type	type	NOUN
ajst-20775	11	18	of	of	ADP
ajst-20775	11	19	detection	detection	NOUN
ajst-20775	11	20	parameters	parameter	NOUN
ajst-20775	11	21	:	:	PUNCT
ajst-20775	11	22	detection	detection	NOUN
ajst-20775	11	23	based	base	VERB
ajst-20775	11	24	on	on	ADP
ajst-20775	11	25	vehicle	vehicle	NOUN
ajst-20775	11	26	behavior	behavior	NOUN
ajst-20775	11	27	characteristics[2	characteristics[2	NOUN
ajst-20775	11	28	]	]	X
ajst-20775	11	29	,	,	PUNCT
ajst-20775	11	30	detection	detection	NOUN
ajst-20775	11	31	based	base	VERB
ajst-20775	11	32	on	on	ADP
ajst-20775	11	33	physiological	physiological	ADJ
ajst-20775	11	34	characteristics	characteristic	NOUN
ajst-20775	11	35	of	of	ADP
ajst-20775	11	36	the	the	DET
ajst-20775	11	37	driver[3	driver[3	NOUN
ajst-20775	11	38	]	]	X
ajst-20775	11	39	,	,	PUNCT
ajst-20775	11	40	and	and	CCONJ
ajst-20775	11	41	detection	detection	NOUN
ajst-20775	11	42	based	base	VERB
ajst-20775	11	43	on	on	ADP
ajst-20775	11	44	facial	facial	ADJ
ajst-20775	11	45	features	feature	NOUN
ajst-20775	11	46	of	of	ADP
ajst-20775	11	47	the	the	DET
ajst-20775	11	48	driver	driver	NOUN
ajst-20775	11	49	.	.	PUNCT
ajst-20775	12	1	detection	detection	NOUN
ajst-20775	12	2	of	of	ADP
ajst-20775	12	3	driver	driver	NOUN
ajst-20775	12	4	fatigue	fatigue	NOUN
ajst-20775	12	5	using	use	VERB
ajst-20775	12	6	facial	facial	ADJ
ajst-20775	12	7	features	feature	NOUN
ajst-20775	12	8	is	be	AUX
ajst-20775	12	9	more	more	ADV
ajst-20775	12	10	practical	practical	ADJ
ajst-20775	12	11	and	and	CCONJ
ajst-20775	12	12	promising	promising	ADJ
ajst-20775	12	13	compared	compare	VERB
ajst-20775	12	14	to	to	ADP
ajst-20775	12	15	methods	method	NOUN
ajst-20775	12	16	based	base	VERB
ajst-20775	12	17	on	on	ADP
ajst-20775	12	18	vehicle	vehicle	NOUN
ajst-20775	12	19	behavior	behavior	NOUN
ajst-20775	12	20	and	and	CCONJ
ajst-20775	12	21	physiological	physiological	ADJ
ajst-20775	12	22	characteristics	characteristic	NOUN
ajst-20775	12	23	,	,	PUNCT
ajst-20775	12	24	as	as	SCONJ
ajst-20775	12	25	it	it	PRON
ajst-20775	12	26	is	be	AUX
ajst-20775	12	27	non	non	ADJ
ajst-20775	12	28	-	-	ADJ
ajst-20775	12	29	contact	contact	ADJ
ajst-20775	12	30	,	,	PUNCT
ajst-20775	12	31	real	real	ADJ
ajst-20775	12	32	-	-	PUNCT
ajst-20775	12	33	time	time	NOUN
ajst-20775	12	34	,	,	PUNCT
ajst-20775	12	35	accurate	accurate	ADJ
ajst-20775	12	36	,	,	PUNCT
ajst-20775	12	37	and	and	CCONJ
ajst-20775	12	38	low	low	ADJ
ajst-20775	12	39	-	-	PUNCT
ajst-20775	12	40	cost	cost	NOUN
ajst-20775	12	41	.	.	PUNCT
ajst-20775	13	1	yi	yi	PROPN
ajst-20775	13	2	et	et	PROPN
ajst-20775	13	3	al.[4]used	al.[4]use	VERB
ajst-20775	13	4	the	the	DET
ajst-20775	13	5	dlib	dlib	NOUN
ajst-20775	13	6	library	library	NOUN
ajst-20775	13	7	to	to	PART
ajst-20775	13	8	evaluate	evaluate	VERB
ajst-20775	13	9	driver	driver	NOUN
ajst-20775	13	10	fatigue	fatigue	NOUN
ajst-20775	13	11	by	by	ADP
ajst-20775	13	12	measuring	measure	VERB
ajst-20775	13	13	facial	facial	ADJ
ajst-20775	13	14	landmarks	landmark	NOUN
ajst-20775	13	15	,	,	PUNCT
ajst-20775	13	16	blink	blink	NOUN
ajst-20775	13	17	frequency	frequency	NOUN
ajst-20775	13	18	,	,	PUNCT
ajst-20775	13	19	eyelid	eyelid	NOUN
ajst-20775	13	20	closure	closure	NOUN
ajst-20775	13	21	,	,	PUNCT
ajst-20775	13	22	yawning	yawn	VERB
ajst-20775	13	23	,	,	PUNCT
ajst-20775	13	24	and	and	CCONJ
ajst-20775	13	25	nodding	nod	VERB
ajst-20775	13	26	.	.	PUNCT
ajst-20775	14	1	aicha	aicha	PROPN
ajst-20775	14	2	et	et	PROPN
ajst-20775	14	3	al.[5]analyzed	al.[5]analyze	VERB
ajst-20775	14	4	fatigue	fatigue	NOUN
ajst-20775	14	5	using	use	VERB
ajst-20775	14	6	the	the	DET
ajst-20775	14	7	eye	eye	NOUN
ajst-20775	14	8	and	and	CCONJ
ajst-20775	14	9	mouth	mouth	NOUN
ajst-20775	14	10	aspect	aspect	NOUN
ajst-20775	14	11	ratios	ratio	NOUN
ajst-20775	14	12	and	and	CCONJ
ajst-20775	14	13	head	head	NOUN
ajst-20775	14	14	movement	movement	NOUN
ajst-20775	14	15	,	,	PUNCT
ajst-20775	14	16	applying	apply	VERB
ajst-20775	14	17	machine	machine	NOUN
ajst-20775	14	18	learning	learning	NOUN
ajst-20775	14	19	techniques	technique	NOUN
ajst-20775	14	20	like	like	ADP
ajst-20775	14	21	mlp	mlp	NOUN
ajst-20775	14	22	and	and	CCONJ
ajst-20775	14	23	knns	knn	NOUN
ajst-20775	14	24	.	.	PUNCT
ajst-20775	15	1	however	however	ADV
ajst-20775	15	2	,	,	PUNCT
ajst-20775	15	3	determining	determine	VERB
ajst-20775	15	4	driver	driver	NOUN
ajst-20775	15	5	fatigue	fatigue	NOUN
ajst-20775	15	6	through	through	ADP
ajst-20775	15	7	blink	blink	ADJ
ajst-20775	15	8	rate	rate	NOUN
ajst-20775	15	9	and	and	CCONJ
ajst-20775	15	10	frequency	frequency	NOUN
ajst-20775	15	11	involves	involve	NOUN
ajst-20775	15	12	predefined	predefine	VERB
ajst-20775	15	13	,	,	PUNCT
ajst-20775	15	14	driver	driver	NOUN
ajst-20775	15	15	-	-	PUNCT
ajst-20775	15	16	specific	specific	ADJ
ajst-20775	15	17	thresholds	threshold	NOUN
ajst-20775	15	18	that	that	PRON
ajst-20775	15	19	limit	limit	VERB
ajst-20775	15	20	the	the	DET
ajst-20775	15	21	method	method	NOUN
ajst-20775	15	22	's	's	PART
ajst-20775	15	23	general	general	ADJ
ajst-20775	15	24	applicability	applicability	NOUN
ajst-20775	15	25	and	and	CCONJ
ajst-20775	15	26	resistance	resistance	NOUN
ajst-20775	15	27	to	to	AUX
ajst-20775	15	28	interference	interference	NOUN
ajst-20775	15	29	.	.	PUNCT
ajst-20775	16	1	moreover	moreover	ADV
ajst-20775	16	2	,	,	PUNCT
ajst-20775	16	3	machine	machine	NOUN
ajst-20775	16	4	learning	learning	NOUN
ajst-20775	16	5	methods	method	NOUN
ajst-20775	16	6	often	often	ADV
ajst-20775	16	7	fail	fail	VERB
ajst-20775	16	8	to	to	PART
ajst-20775	16	9	automatically	automatically	ADV
ajst-20775	16	10	learn	learn	VERB
ajst-20775	16	11	drowsiness	drowsiness	NOUN
ajst-20775	16	12	features	feature	VERB
ajst-20775	16	13	from	from	ADP
ajst-20775	16	14	videos	video	NOUN
ajst-20775	16	15	,	,	PUNCT
ajst-20775	16	16	decreasing	decrease	VERB
ajst-20775	16	17	their	their	PRON
ajst-20775	16	18	effectiveness	effectiveness	NOUN
ajst-20775	16	19	in	in	ADP
ajst-20775	16	20	complex	complex	ADJ
ajst-20775	16	21	scenarios	scenario	NOUN
ajst-20775	16	22	.	.	PUNCT
ajst-20775	17	1	dua	dua	PROPN
ajst-20775	17	2	et	et	PROPN
ajst-20775	17	3	al.[6]proposed	al.[6]propose	VERB
ajst-20775	17	4	an	an	DET
ajst-20775	17	5	ensemble	ensemble	ADJ
ajst-20775	17	6	learning	learning	NOUN
ajst-20775	17	7	model	model	NOUN
ajst-20775	17	8	to	to	PART
ajst-20775	17	9	assess	assess	VERB
ajst-20775	17	10	the	the	DET
ajst-20775	17	11	driver	driver	NOUN
ajst-20775	17	12	's	's	PART
ajst-20775	17	13	state	state	NOUN
ajst-20775	17	14	,	,	PUNCT
ajst-20775	17	15	which	which	PRON
ajst-20775	17	16	,	,	PUNCT
ajst-20775	17	17	despite	despite	SCONJ
ajst-20775	17	18	showing	show	VERB
ajst-20775	17	19	excellent	excellent	ADJ
ajst-20775	17	20	performance	performance	NOUN
ajst-20775	17	21	in	in	ADP
ajst-20775	17	22	improving	improve	VERB
ajst-20775	17	23	accuracy	accuracy	NOUN
ajst-20775	17	24	,	,	PUNCT
ajst-20775	17	25	possesses	possess	VERB
ajst-20775	17	26	over	over	ADP
ajst-20775	17	27	300	300	NUM
ajst-20775	17	28	million	million	NUM
ajst-20775	17	29	trainable	trainable	ADJ
ajst-20775	17	30	parameters	parameter	NOUN
ajst-20775	17	31	,	,	PUNCT
ajst-20775	17	32	requiring	require	VERB
ajst-20775	17	33	substantial	substantial	ADJ
ajst-20775	17	34	computational	computational	ADJ
ajst-20775	17	35	power	power	NOUN
ajst-20775	17	36	support	support	NOUN
ajst-20775	17	37	,	,	PUNCT
ajst-20775	17	38	thereby	thereby	ADV
ajst-20775	17	39	limiting	limit	VERB
ajst-20775	17	40	its	its	PRON
ajst-20775	17	41	practical	practical	ADJ
ajst-20775	17	42	applicability	applicability	NOUN
ajst-20775	17	43	.	.	PUNCT
ajst-20775	18	1	liu	liu	PROPN
ajst-20775	18	2	et	et	PROPN
ajst-20775	18	3	al.[7]proposed	al.[7]propose	VERB
ajst-20775	18	4	a	a	DET
ajst-20775	18	5	fatigue	fatigue	NOUN
ajst-20775	18	6	detection	detection	NOUN
ajst-20775	18	7	method	method	NOUN
ajst-20775	18	8	using	use	VERB
ajst-20775	18	9	cnn	cnn	PROPN
ajst-20775	18	10	-	-	PUNCT
ajst-20775	18	11	lstm	lstm	PROPN
ajst-20775	18	12	,	,	PUNCT
ajst-20775	18	13	which	which	PRON
ajst-20775	18	14	utilizes	utilize	VERB
ajst-20775	18	15	cnn	cnn	PROPN
ajst-20775	18	16	to	to	PART
ajst-20775	18	17	extract	extract	VERB
ajst-20775	18	18	features	feature	NOUN
ajst-20775	18	19	related	relate	VERB
ajst-20775	18	20	to	to	ADP
ajst-20775	18	21	the	the	DET
ajst-20775	18	22	eyes	eye	NOUN
ajst-20775	18	23	,	,	PUNCT
ajst-20775	18	24	mouth	mouth	NOUN
ajst-20775	18	25	,	,	PUNCT
ajst-20775	18	26	and	and	CCONJ
ajst-20775	18	27	facial	facial	ADJ
ajst-20775	18	28	orientation	orientation	NOUN
ajst-20775	18	29	,	,	PUNCT
ajst-20775	18	30	and	and	CCONJ
ajst-20775	18	31	then	then	ADV
ajst-20775	18	32	combines	combine	VERB
ajst-20775	18	33	these	these	PRON
ajst-20775	18	34	with	with	ADP
ajst-20775	18	35	steering	steering	NOUN
ajst-20775	18	36	wheel	wheel	NOUN
ajst-20775	18	37	feature	feature	NOUN
ajst-20775	18	38	parameters	parameter	NOUN
ajst-20775	18	39	sa	sa	VERB
ajst-20775	18	40	as	as	ADP
ajst-20775	18	41	inputs	input	NOUN
ajst-20775	18	42	to	to	ADP
ajst-20775	18	43	the	the	DET
ajst-20775	18	44	lstm	lstm	NOUN
ajst-20775	18	45	,	,	PUNCT
ajst-20775	18	46	with	with	ADP
ajst-20775	18	47	the	the	DET
ajst-20775	18	48	level	level	NOUN
ajst-20775	18	49	of	of	ADP
ajst-20775	18	50	fatigue	fatigue	NOUN
ajst-20775	18	51	as	as	ADP
ajst-20775	18	52	the	the	DET
ajst-20775	18	53	output	output	NOUN
ajst-20775	18	54	.	.	PUNCT
ajst-20775	19	1	although	although	SCONJ
ajst-20775	19	2	the	the	DET
ajst-20775	19	3	lstm	lstm	NOUN
ajst-20775	19	4	network	network	NOUN
ajst-20775	19	5	is	be	AUX
ajst-20775	19	6	capable	capable	ADJ
ajst-20775	19	7	of	of	ADP
ajst-20775	19	8	extracting	extract	VERB
ajst-20775	19	9	temporal	temporal	ADJ
ajst-20775	19	10	features	feature	NOUN
ajst-20775	19	11	from	from	ADP
ajst-20775	19	12	the	the	DET
ajst-20775	19	13	input	input	NOUN
ajst-20775	19	14	data	datum	NOUN
ajst-20775	19	15	,	,	PUNCT
ajst-20775	19	16	enhancing	enhance	VERB
ajst-20775	19	17	the	the	DET
ajst-20775	19	18	accuracy	accuracy	NOUN
ajst-20775	19	19	of	of	ADP
ajst-20775	19	20	driver	driver	NOUN
ajst-20775	19	21	drowsiness	drowsiness	NOUN
ajst-20775	19	22	detection	detection	NOUN
ajst-20775	19	23	to	to	ADP
ajst-20775	19	24	some	some	DET
ajst-20775	19	25	extent	extent	NOUN
ajst-20775	19	26	,	,	PUNCT
ajst-20775	19	27	it	it	PRON
ajst-20775	19	28	is	be	AUX
ajst-20775	19	29	better	well	ADV
ajst-20775	19	30	suited	suited	ADJ
ajst-20775	19	31	for	for	ADP
ajst-20775	19	32	processing	process	VERB
ajst-20775	19	33	short	short	ADJ
ajst-20775	19	34	-	-	PUNCT
ajst-20775	19	35	term	term	NOUN
ajst-20775	19	36	memory	memory	NOUN
ajst-20775	19	37	and	and	CCONJ
ajst-20775	19	38	short	short	ADJ
ajst-20775	19	39	-	-	PUNCT
ajst-20775	19	40	term	term	NOUN
ajst-20775	19	41	temporal	temporal	ADJ
ajst-20775	19	42	dependencies	dependency	NOUN
ajst-20775	19	43	.	.	PUNCT
ajst-20775	20	1	the	the	DET
ajst-20775	20	2	main	main	ADJ
ajst-20775	20	3	contributions	contribution	NOUN
ajst-20775	20	4	of	of	ADP
ajst-20775	20	5	this	this	DET
ajst-20775	20	6	paper	paper	NOUN
ajst-20775	20	7	are	be	AUX
ajst-20775	20	8	as	as	SCONJ
ajst-20775	20	9	follows	follow	VERB
ajst-20775	20	10	:	:	PUNCT
ajst-20775	20	11	1	1	X
ajst-20775	20	12	)	)	PUNCT
ajst-20775	20	13	a	a	DET
ajst-20775	20	14	lightweight	lightweight	ADJ
ajst-20775	20	15	gm	gm	PROPN
ajst-20775	20	16	feature	feature	NOUN
ajst-20775	20	17	extraction	extraction	NOUN
ajst-20775	20	18	module	module	NOUN
ajst-20775	20	19	is	be	AUX
ajst-20775	20	20	designed	design	VERB
ajst-20775	20	21	,	,	PUNCT
ajst-20775	20	22	and	and	CCONJ
ajst-20775	20	23	based	base	VERB
ajst-20775	20	24	on	on	ADP
ajst-20775	20	25	this	this	DET
ajst-20775	20	26	module	module	NOUN
ajst-20775	20	27	,	,	PUNCT
ajst-20775	20	28	a	a	DET
ajst-20775	20	29	multi	multi	ADJ
ajst-20775	20	30	-	-	ADJ
ajst-20775	20	31	scale	scale	ADJ
ajst-20775	20	32	fusion	fusion	NOUN
ajst-20775	20	33	facial	facial	ADJ
ajst-20775	20	34	landmark	landmark	NOUN
ajst-20775	20	35	detection	detection	NOUN
ajst-20775	20	36	algorithm	algorithm	NOUN
ajst-20775	20	37	is	be	AUX
ajst-20775	20	38	constructed	construct	VERB
ajst-20775	20	39	.	.	PUNCT
ajst-20775	21	1	this	this	DET
ajst-20775	21	2	algorithm	algorithm	NOUN
ajst-20775	21	3	extracts	extract	VERB
ajst-20775	21	4	fatigue	fatigue	NOUN
ajst-20775	21	5	feature	feature	NOUN
ajst-20775	21	6	parameters	parameter	NOUN
ajst-20775	21	7	from	from	ADP
ajst-20775	21	8	the	the	DET
ajst-20775	21	9	driver	driver	NOUN
ajst-20775	21	10	's	's	PART
ajst-20775	21	11	facial	facial	ADJ
ajst-20775	21	12	temporal	temporal	ADJ
ajst-20775	21	13	sequence	sequence	NOUN
ajst-20775	21	14	through	through	ADP
ajst-20775	21	15	facial	facial	ADJ
ajst-20775	21	16	landmarks	landmark	NOUN
ajst-20775	21	17	;	;	PUNCT
ajst-20775	21	18	2	2	X
ajst-20775	21	19	)	)	PUNCT
ajst-20775	21	20	the	the	DET
ajst-20775	21	21	encoder	encoder	NOUN
ajst-20775	21	22	part	part	NOUN
ajst-20775	21	23	of	of	ADP
ajst-20775	21	24	the	the	DET
ajst-20775	21	25	transformer	transformer	NOUN
ajst-20775	21	26	model	model	NOUN
ajst-20775	21	27	is	be	AUX
ajst-20775	21	28	applied	apply	VERB
ajst-20775	21	29	to	to	ADP
ajst-20775	21	30	the	the	DET
ajst-20775	21	31	classification	classification	NOUN
ajst-20775	21	32	of	of	ADP
ajst-20775	21	33	fatigue	fatigue	NOUN
ajst-20775	21	34	driving	drive	VERB
ajst-20775	21	35	temporal	temporal	ADJ
ajst-20775	21	36	sequences	sequence	NOUN
ajst-20775	21	37	,	,	PUNCT
ajst-20775	21	38	thereby	thereby	ADV
ajst-20775	21	39	effectively	effectively	ADV
ajst-20775	21	40	capturing	capture	VERB
ajst-20775	21	41	the	the	DET
ajst-20775	21	42	temporal	temporal	ADJ
ajst-20775	21	43	characteristics	characteristic	NOUN
ajst-20775	21	44	of	of	ADP
ajst-20775	21	45	fatigue	fatigue	NOUN
ajst-20775	21	46	;	;	PUNCT
ajst-20775	21	47	3	3	X
ajst-20775	21	48	)	)	PUNCT
ajst-20775	21	49	experiments	experiment	NOUN
ajst-20775	21	50	on	on	ADP
ajst-20775	21	51	facial	facial	ADJ
ajst-20775	21	52	landmarks	landmark	NOUN
ajst-20775	21	53	and	and	CCONJ
ajst-20775	21	54	fatigue	fatigue	NOUN
ajst-20775	21	55	driving	drive	VERB
ajst-20775	21	56	detection	detection	NOUN
ajst-20775	21	57	datasets	dataset	NOUN
ajst-20775	21	58	validate	validate	VERB
ajst-20775	21	59	the	the	DET
ajst-20775	21	60	effectiveness	effectiveness	NOUN
ajst-20775	21	61	of	of	ADP
ajst-20775	21	62	the	the	DET
ajst-20775	21	63	proposed	propose	VERB
ajst-20775	21	64	method	method	NOUN
ajst-20775	21	65	.	.	PUNCT
ajst-20775	22	1	2	2	X
ajst-20775	22	2	.	.	X
ajst-20775	22	3	methodology	methodology	NOUN
ajst-20775	22	4	the	the	DET
ajst-20775	22	5	temporal	temporal	ADJ
ajst-20775	22	6	sequence	sequence	NOUN
ajst-20775	22	7	transformer	transformer	NOUN
ajst-20775	22	8	-	-	PUNCT
ajst-20775	22	9	based	base	VERB
ajst-20775	22	10	fatigue	fatigue	NOUN
ajst-20775	22	11	driving	drive	VERB
ajst-20775	22	12	detection	detection	NOUN
ajst-20775	22	13	method	method	NOUN
ajst-20775	22	14	primarily	primarily	ADV
ajst-20775	22	15	consists	consist	VERB
ajst-20775	22	16	of	of	ADP
ajst-20775	22	17	three	three	NUM
ajst-20775	22	18	steps	step	NOUN
ajst-20775	22	19	:	:	PUNCT
ajst-20775	22	20	face	face	NOUN
ajst-20775	22	21	detection	detection	NOUN
ajst-20775	22	22	,	,	PUNCT
ajst-20775	22	23	facial	facial	ADJ
ajst-20775	22	24	landmark	landmark	NOUN
ajst-20775	22	25	localization	localization	NOUN
ajst-20775	22	26	,	,	PUNCT
ajst-20775	22	27	and	and	CCONJ
ajst-20775	22	28	fatigue	fatigue	NOUN
ajst-20775	22	29	state	state	NOUN
ajst-20775	22	30	assessment	assessment	NOUN
ajst-20775	22	31	.	.	PUNCT
ajst-20775	23	1	initially	initially	ADV
ajst-20775	23	2	,	,	PUNCT
ajst-20775	23	3	the	the	DET
ajst-20775	23	4	driver	driver	NOUN
ajst-20775	23	5	's	's	PART
ajst-20775	23	6	face	face	NOUN
ajst-20775	23	7	is	be	AUX
ajst-20775	23	8	acquired	acquire	VERB
ajst-20775	23	9	using	use	VERB
ajst-20775	23	10	the	the	DET
ajst-20775	23	11	retinaface[8	retinaface[8	NOUN
ajst-20775	23	12	]	]	PUNCT
ajst-20775	23	13	face	face	NOUN
ajst-20775	23	14	detection	detection	NOUN
ajst-20775	23	15	algorithm	algorithm	NOUN
ajst-20775	23	16	.	.	PUNCT
ajst-20775	24	1	then	then	ADV
ajst-20775	24	2	,	,	PUNCT
ajst-20775	24	3	based	base	VERB
ajst-20775	24	4	on	on	ADP
ajst-20775	24	5	the	the	DET
ajst-20775	24	6	detected	detect	VERB
ajst-20775	24	7	facial	facial	ADJ
ajst-20775	24	8	region	region	NOUN
ajst-20775	24	9	,	,	PUNCT
ajst-20775	24	10	the	the	DET
ajst-20775	24	11	driver	driver	NOUN
ajst-20775	24	12	's	's	PART
ajst-20775	24	13	facial	facial	ADJ
ajst-20775	24	14	landmarks	landmark	NOUN
ajst-20775	24	15	are	be	AUX
ajst-20775	24	16	localized	localize	VERB
ajst-20775	24	17	using	use	VERB
ajst-20775	24	18	the	the	DET
ajst-20775	24	19	landmark	landmark	NOUN
ajst-20775	24	20	detection	detection	NOUN
ajst-20775	24	21	network	network	NOUN
ajst-20775	24	22	msgm	msgm	NOUN
ajst-20775	24	23	-	-	PUNCT
ajst-20775	24	24	net	net	NOUN
ajst-20775	24	25	,	,	PUNCT
ajst-20775	24	26	and	and	CCONJ
ajst-20775	24	27	a	a	DET
ajst-20775	24	28	temporal	temporal	ADJ
ajst-20775	24	29	sequence	sequence	NOUN
ajst-20775	24	30	of	of	ADP
ajst-20775	24	31	fatigue	fatigue	NOUN
ajst-20775	24	32	feature	feature	NOUN
ajst-20775	24	33	parameters	parameter	NOUN
ajst-20775	24	34	is	be	AUX
ajst-20775	24	35	calculated	calculate	VERB
ajst-20775	24	36	based	base	VERB
ajst-20775	24	37	on	on	ADP
ajst-20775	24	38	these	these	DET
ajst-20775	24	39	facial	facial	ADJ
ajst-20775	24	40	landmarks	landmark	NOUN
ajst-20775	24	41	.	.	PUNCT
ajst-20775	25	1	finally	finally	ADV
ajst-20775	25	2	,	,	PUNCT
ajst-20775	25	3	the	the	DET
ajst-20775	25	4	temporal	temporal	ADJ
ajst-20775	25	5	sequence	sequence	NOUN
ajst-20775	25	6	of	of	ADP
ajst-20775	25	7	fatigue	fatigue	NOUN
ajst-20775	25	8	feature	feature	NOUN
ajst-20775	25	9	parameters	parameter	NOUN
ajst-20775	25	10	is	be	AUX
ajst-20775	25	11	fed	feed	VERB
ajst-20775	25	12	into	into	ADP
ajst-20775	25	13	the	the	DET
ajst-20775	25	14	transformer	transformer	NOUN
ajst-20775	25	15	classification	classification	NOUN
ajst-20775	25	16	method	method	NOUN
ajst-20775	25	17	.	.	PUNCT
ajst-20775	26	1	38	38	NUM
ajst-20775	26	2	2.1	2.1	NUM
ajst-20775	26	3	.	.	PUNCT
ajst-20775	27	1	msgm	msgm	NOUN
ajst-20775	27	2	-	-	PUNCT
ajst-20775	27	3	net	net	ADJ
ajst-20775	27	4	facial	facial	ADJ
ajst-20775	27	5	landmark	landmark	NOUN
ajst-20775	27	6	detection	detection	NOUN
ajst-20775	27	7	algorithm	algorithm	NOUN
ajst-20775	27	8	after	after	ADP
ajst-20775	27	9	acquiring	acquire	VERB
ajst-20775	27	10	the	the	DET
ajst-20775	27	11	facial	facial	ADJ
ajst-20775	27	12	region	region	NOUN
ajst-20775	27	13	of	of	ADP
ajst-20775	27	14	the	the	DET
ajst-20775	27	15	driver	driver	NOUN
ajst-20775	27	16	,	,	PUNCT
ajst-20775	27	17	a	a	DET
ajst-20775	27	18	further	further	ADJ
ajst-20775	27	19	step	step	NOUN
ajst-20775	27	20	involves	involve	VERB
ajst-20775	27	21	extracting	extract	VERB
ajst-20775	27	22	98	98	NUM
ajst-20775	27	23	landmarks	landmark	NOUN
ajst-20775	27	24	from	from	ADP
ajst-20775	27	25	that	that	DET
ajst-20775	27	26	area	area	NOUN
ajst-20775	27	27	.	.	PUNCT
ajst-20775	28	1	the	the	DET
ajst-20775	28	2	efficiency	efficiency	NOUN
ajst-20775	28	3	of	of	ADP
ajst-20775	28	4	facial	facial	ADJ
ajst-20775	28	5	landmark	landmark	NOUN
ajst-20775	28	6	detection	detection	NOUN
ajst-20775	28	7	hinges	hinge	NOUN
ajst-20775	28	8	on	on	ADP
ajst-20775	28	9	the	the	DET
ajst-20775	28	10	design	design	NOUN
ajst-20775	28	11	of	of	ADP
ajst-20775	28	12	the	the	DET
ajst-20775	28	13	backbone	backbone	NOUN
ajst-20775	28	14	network	network	NOUN
ajst-20775	28	15	,	,	PUNCT
ajst-20775	28	16	which	which	PRON
ajst-20775	28	17	can	can	AUX
ajst-20775	28	18	both	both	PRON
ajst-20775	28	19	increase	increase	VERB
ajst-20775	28	20	processing	process	VERB
ajst-20775	28	21	speed	speed	NOUN
ajst-20775	28	22	and	and	CCONJ
ajst-20775	28	23	alleviate	alleviate	VERB
ajst-20775	28	24	the	the	DET
ajst-20775	28	25	model	model	NOUN
ajst-20775	28	26	's	's	PART
ajst-20775	28	27	computational	computational	ADJ
ajst-20775	28	28	load	load	NOUN
ajst-20775	28	29	,	,	PUNCT
ajst-20775	28	30	thus	thus	ADV
ajst-20775	28	31	facilitating	facilitate	VERB
ajst-20775	28	32	efficient	efficient	ADJ
ajst-20775	28	33	and	and	CCONJ
ajst-20775	28	34	rapid	rapid	ADJ
ajst-20775	28	35	inference	inference	NOUN
ajst-20775	28	36	.	.	PUNCT
ajst-20775	29	1	mobileone[9	mobileone[9	NUM
ajst-20775	29	2	]	]	PUNCT
ajst-20775	29	3	,	,	PUNCT
ajst-20775	29	4	developed	develop	VERB
ajst-20775	29	5	by	by	ADP
ajst-20775	29	6	apple	apple	PROPN
ajst-20775	29	7	inc	inc	PROPN
ajst-20775	29	8	.	.	PROPN
ajst-20775	29	9	,	,	PUNCT
ajst-20775	29	10	is	be	AUX
ajst-20775	29	11	an	an	DET
ajst-20775	29	12	optimized	optimize	VERB
ajst-20775	29	13	backbone	backbone	NOUN
ajst-20775	29	14	network	network	NOUN
ajst-20775	29	15	for	for	ADP
ajst-20775	29	16	mobile	mobile	ADJ
ajst-20775	29	17	devices	device	NOUN
ajst-20775	29	18	that	that	PRON
ajst-20775	29	19	uses	use	VERB
ajst-20775	29	20	structural	structural	ADJ
ajst-20775	29	21	re	re	NOUN
ajst-20775	29	22	-	-	NOUN
ajst-20775	29	23	parameterization	parameterization	NOUN
ajst-20775	29	24	and	and	CCONJ
ajst-20775	29	25	a	a	DET
ajst-20775	29	26	branching	branch	VERB
ajst-20775	29	27	topology	topology	NOUN
ajst-20775	29	28	to	to	PART
ajst-20775	29	29	enhance	enhance	VERB
ajst-20775	29	30	inference	inference	NOUN
ajst-20775	29	31	speed	speed	NOUN
ajst-20775	29	32	.	.	PUNCT
ajst-20775	30	1	this	this	DET
ajst-20775	30	2	paper	paper	NOUN
ajst-20775	30	3	develops	develop	VERB
ajst-20775	30	4	a	a	DET
ajst-20775	30	5	lightweight	lightweight	ADJ
ajst-20775	30	6	feature	feature	NOUN
ajst-20775	30	7	extraction	extraction	NOUN
ajst-20775	30	8	gm	gm	PROPN
ajst-20775	30	9	module	module	NOUN
ajst-20775	30	10	based	base	VERB
ajst-20775	30	11	on	on	ADP
ajst-20775	30	12	mobileone	mobileone	NOUN
ajst-20775	30	13	,	,	PUNCT
ajst-20775	30	14	incorporating	incorporate	VERB
ajst-20775	30	15	phantom	phantom	ADJ
ajst-20775	30	16	channels	channel	NOUN
ajst-20775	30	17	from	from	ADP
ajst-20775	30	18	the	the	DET
ajst-20775	30	19	ghost[10	ghost[10	PROPN
ajst-20775	30	20	]	]	PUNCT
ajst-20775	30	21	module	module	NOUN
ajst-20775	30	22	to	to	PART
ajst-20775	30	23	increase	increase	VERB
ajst-20775	30	24	inference	inference	NOUN
ajst-20775	30	25	speed	speed	NOUN
ajst-20775	30	26	without	without	ADP
ajst-20775	30	27	reducing	reduce	VERB
ajst-20775	30	28	feature	feature	NOUN
ajst-20775	30	29	map	map	NOUN
ajst-20775	30	30	output	output	NOUN
ajst-20775	30	31	.	.	PUNCT
ajst-20775	31	1	the	the	DET
ajst-20775	31	2	structure	structure	NOUN
ajst-20775	31	3	of	of	ADP
ajst-20775	31	4	the	the	DET
ajst-20775	31	5	gm	gm	PROPN
ajst-20775	31	6	module	module	NOUN
ajst-20775	31	7	is	be	AUX
ajst-20775	31	8	depicted	depict	VERB
ajst-20775	31	9	in	in	ADP
ajst-20775	31	10	figure	figure	NOUN
ajst-20775	31	11	2	2	NUM
ajst-20775	31	12	,	,	PUNCT
ajst-20775	31	13	divided	divide	VERB
ajst-20775	31	14	into	into	ADP
ajst-20775	31	15	the	the	DET
ajst-20775	31	16	training	training	NOUN
ajst-20775	31	17	phase	phase	NOUN
ajst-20775	31	18	and	and	CCONJ
ajst-20775	31	19	the	the	DET
ajst-20775	31	20	inference	inference	NOUN
ajst-20775	31	21	phase	phase	NOUN
ajst-20775	31	22	.	.	PUNCT
ajst-20775	32	1	figure	figure	NOUN
ajst-20775	32	2	2(a	2(a	NUM
ajst-20775	32	3	)	)	PUNCT
ajst-20775	32	4	illustrates	illustrate	VERB
ajst-20775	32	5	the	the	DET
ajst-20775	32	6	training	training	NOUN
ajst-20775	32	7	structure	structure	NOUN
ajst-20775	32	8	based	base	VERB
ajst-20775	32	9	on	on	ADP
ajst-20775	32	10	the	the	DET
ajst-20775	32	11	gm	gm	PROPN
ajst-20775	32	12	module	module	NOUN
ajst-20775	32	13	.	.	PUNCT
ajst-20775	33	1	figure	figure	NOUN
ajst-20775	33	2	2(b	2(b	NUM
ajst-20775	33	3	)	)	PUNCT
ajst-20775	33	4	shows	show	VERB
ajst-20775	33	5	the	the	DET
ajst-20775	33	6	inference	inference	NOUN
ajst-20775	33	7	structure	structure	NOUN
ajst-20775	33	8	after	after	ADP
ajst-20775	33	9	structural	structural	ADJ
ajst-20775	33	10	re	re	NOUN
ajst-20775	33	11	-	-	NOUN
ajst-20775	33	12	parameterization	parameterization	NOUN
ajst-20775	33	13	.	.	PUNCT
ajst-20775	34	1	figure	figure	NOUN
ajst-20775	34	2	2	2	NUM
ajst-20775	34	3	.	.	PUNCT
ajst-20775	34	4	gm	gm	PROPN
ajst-20775	34	5	module	module	NOUN
ajst-20775	34	6	structure	structure	NOUN
ajst-20775	34	7	diagram	diagram	VERB
ajst-20775	34	8	the	the	DET
ajst-20775	34	9	gm	gm	PROPN
ajst-20775	34	10	bottleneck	bottleneck	NOUN
ajst-20775	34	11	layer	layer	NOUN
ajst-20775	34	12	structure	structure	NOUN
ajst-20775	34	13	is	be	AUX
ajst-20775	34	14	shown	show	VERB
ajst-20775	34	15	in	in	ADP
ajst-20775	34	16	figure	figure	NOUN
ajst-20775	34	17	4	4	NUM
ajst-20775	34	18	,	,	PUNCT
ajst-20775	34	19	composed	compose	VERB
ajst-20775	34	20	by	by	ADP
ajst-20775	34	21	stacking	stack	VERB
ajst-20775	34	22	gm	gm	PROPN
ajst-20775	34	23	modules	module	NOUN
ajst-20775	34	24	.	.	PUNCT
ajst-20775	35	1	figure	figure	NOUN
ajst-20775	35	2	3(a	3(a	NUM
ajst-20775	35	3	)	)	PUNCT
ajst-20775	35	4	depicts	depict	VERB
ajst-20775	35	5	the	the	DET
ajst-20775	35	6	structure	structure	NOUN
ajst-20775	35	7	of	of	ADP
ajst-20775	35	8	the	the	DET
ajst-20775	35	9	gm	gm	NOUN
ajst-20775	35	10	-	-	PUNCT
ajst-20775	35	11	bottleneck	bottleneck	NOUN
ajst-20775	35	12	with	with	ADP
ajst-20775	35	13	a	a	DET
ajst-20775	35	14	stride	stride	NOUN
ajst-20775	35	15	of	of	ADP
ajst-20775	35	16	1	1	NUM
ajst-20775	35	17	.	.	PUNCT
ajst-20775	35	18	figure	figure	NOUN
ajst-20775	35	19	3(b	3(b	NUM
ajst-20775	35	20	)	)	PUNCT
ajst-20775	35	21	shows	show	VERB
ajst-20775	35	22	the	the	DET
ajst-20775	35	23	structure	structure	NOUN
ajst-20775	35	24	of	of	ADP
ajst-20775	35	25	the	the	DET
ajst-20775	35	26	gm	gm	NOUN
ajst-20775	35	27	-	-	PUNCT
ajst-20775	35	28	bottleneck	bottleneck	NOUN
ajst-20775	35	29	with	with	ADP
ajst-20775	35	30	a	a	DET
ajst-20775	35	31	stride	stride	NOUN
ajst-20775	35	32	of	of	ADP
ajst-20775	35	33	2	2	NUM
ajst-20775	35	34	.	.	PUNCT
ajst-20775	36	1	the	the	DET
ajst-20775	36	2	detailed	detailed	ADJ
ajst-20775	36	3	configuration	configuration	NOUN
ajst-20775	36	4	of	of	ADP
ajst-20775	36	5	the	the	DET
ajst-20775	36	6	multi	multi	ADJ
ajst-20775	36	7	-	-	ADJ
ajst-20775	36	8	scale	scale	ADJ
ajst-20775	36	9	fusion	fusion	NOUN
ajst-20775	36	10	facial	facial	ADJ
ajst-20775	36	11	landmark	landmark	NOUN
ajst-20775	36	12	detection	detection	NOUN
ajst-20775	36	13	network	network	NOUN
ajst-20775	36	14	structure	structure	NOUN
ajst-20775	36	15	is	be	AUX
ajst-20775	36	16	shown	show	VERB
ajst-20775	36	17	in	in	ADP
ajst-20775	36	18	table	table	NOUN
ajst-20775	36	19	i	i	PRON
ajst-20775	36	20	,	,	PUNCT
ajst-20775	36	21	where	where	SCONJ
ajst-20775	36	22	n	n	PRON
ajst-20775	36	23	indicates	indicate	VERB
ajst-20775	36	24	the	the	DET
ajst-20775	36	25	number	number	NOUN
ajst-20775	36	26	of	of	ADP
ajst-20775	36	27	repetitions	repetition	NOUN
ajst-20775	36	28	of	of	ADP
ajst-20775	36	29	that	that	DET
ajst-20775	36	30	layer	layer	NOUN
ajst-20775	36	31	structure	structure	NOUN
ajst-20775	36	32	,	,	PUNCT
ajst-20775	36	33	s	s	X
ajst-20775	36	34	represents	represent	VERB
ajst-20775	36	35	the	the	DET
ajst-20775	36	36	stride	stride	NOUN
ajst-20775	36	37	of	of	ADP
ajst-20775	36	38	the	the	DET
ajst-20775	36	39	first	first	ADJ
ajst-20775	36	40	layer	layer	NOUN
ajst-20775	36	41	's	's	PART
ajst-20775	36	42	convolution	convolution	NOUN
ajst-20775	36	43	,	,	PUNCT
ajst-20775	36	44	c	c	PROPN
ajst-20775	36	45	indicates	indicate	VERB
ajst-20775	36	46	the	the	DET
ajst-20775	36	47	final	final	ADJ
ajst-20775	36	48	output	output	NOUN
ajst-20775	36	49	channel	channel	NOUN
ajst-20775	36	50	number	number	NOUN
ajst-20775	36	51	for	for	ADP
ajst-20775	36	52	that	that	DET
ajst-20775	36	53	row	row	NOUN
ajst-20775	36	54	,	,	PUNCT
ajst-20775	36	55	and	and	CCONJ
ajst-20775	36	56	the	the	DET
ajst-20775	36	57	expansion	expansion	NOUN
ajst-20775	36	58	factor	factor	NOUN
ajst-20775	36	59	t	t	PROPN
ajst-20775	36	60	is	be	AUX
ajst-20775	36	61	used	use	VERB
ajst-20775	36	62	to	to	PART
ajst-20775	36	63	determine	determine	VERB
ajst-20775	36	64	the	the	DET
ajst-20775	36	65	output	output	NOUN
ajst-20775	36	66	channel	channel	NOUN
ajst-20775	36	67	number	number	NOUN
ajst-20775	36	68	applied	apply	VERB
ajst-20775	36	69	to	to	ADP
ajst-20775	36	70	the	the	DET
ajst-20775	36	71	intermediate	intermediate	ADJ
ajst-20775	36	72	layers	layer	NOUN
ajst-20775	36	73	.	.	PUNCT
ajst-20775	37	1	figure	figure	VERB
ajst-20775	37	2	3	3	NUM
ajst-20775	37	3	.	.	PUNCT
ajst-20775	38	1	gm	gm	ADJ
ajst-20775	38	2	-	-	PUNCT
ajst-20775	38	3	bottleneck	bottleneck	NOUN
ajst-20775	38	4	module	module	NOUN
ajst-20775	38	5	structure	structure	NOUN
ajst-20775	38	6	table	table	NOUN
ajst-20775	38	7	1	1	NUM
ajst-20775	38	8	.	.	PUNCT
ajst-20775	39	1	structure	structure	NOUN
ajst-20775	39	2	of	of	ADP
ajst-20775	39	3	landmark	landmark	PROPN
ajst-20775	39	4	detection	detection	NOUN
ajst-20775	39	5	network	network	NOUN
ajst-20775	39	6	input	input	NOUN
ajst-20775	39	7	operator	operator	NOUN
ajst-20775	39	8	t	t	PROPN
ajst-20775	39	9	c	c	PROPN
ajst-20775	40	1	n	n	PROPN
ajst-20775	40	2	s	s	PROPN
ajst-20775	40	3	1122×3	1122×3	NUM
ajst-20775	40	4	conv3×3	conv3×3	NOUN
ajst-20775	40	5	64	64	NUM
ajst-20775	40	6	1	1	NUM
ajst-20775	40	7	2	2	NUM
ajst-20775	40	8	562×64	562×64	NUM
ajst-20775	40	9	conv3×3	conv3×3	NOUN
ajst-20775	40	10	64	64	NUM
ajst-20775	40	11	1	1	NUM
ajst-20775	40	12	1	1	NUM
ajst-20775	40	13	562×64	562×64	NUM
ajst-20775	40	14	gmbottleneck	gmbottleneck	NOUN
ajst-20775	40	15	2	2	NUM
ajst-20775	40	16	80	80	NUM
ajst-20775	40	17	3	3	NUM
ajst-20775	40	18	2	2	NUM
ajst-20775	40	19	282×80	282×80	NUM
ajst-20775	40	20	gmbottleneck	gmbottleneck	NOUN
ajst-20775	40	21	2	2	NUM
ajst-20775	40	22	96	96	NUM
ajst-20775	40	23	3	3	NUM
ajst-20775	40	24	2	2	NUM
ajst-20775	40	25	142×96	142×96	NUM
ajst-20775	40	26	gmbottleneck	gmbottleneck	NOUN
ajst-20775	40	27	4	4	NUM
ajst-20775	40	28	144	144	NUM
ajst-20775	40	29	4	4	NUM
ajst-20775	40	30	2	2	NUM
ajst-20775	40	31	72×144	72×144	NUM
ajst-20775	40	32	gmbottleneck	gmbottleneck	NOUN
ajst-20775	40	33	2	2	NUM
ajst-20775	40	34	16	16	NUM
ajst-20775	40	35	1	1	NUM
ajst-20775	40	36	1	1	NUM
ajst-20775	40	37	72×16	72×16	NUM
ajst-20775	40	38	conv3×3	conv3×3	NOUN
ajst-20775	40	39	32	32	NUM
ajst-20775	40	40	1	1	NUM
ajst-20775	40	41	1	1	NUM
ajst-20775	40	42	72×32	72×32	NUM
ajst-20775	40	43	conv7×7	conv7×7	NOUN
ajst-20775	40	44	128	128	NUM
ajst-20775	40	45	1	1	NUM
ajst-20775	40	46	1	1	NUM
ajst-20775	40	47	(	(	PUNCT
ajst-20775	40	48	s1)562×64	s1)562×64	VERB
ajst-20775	40	49	avgpool	avgpool	NOUN
ajst-20775	40	50	64	64	NUM
ajst-20775	40	51	1	1	NUM
ajst-20775	40	52	(	(	PUNCT
ajst-20775	40	53	s2)282×80	s2)282×80	PROPN
ajst-20775	40	54	avgpool	avgpool	VERB
ajst-20775	40	55	80	80	NUM
ajst-20775	40	56	11	11	NUM
ajst-20775	40	57	(	(	PUNCT
ajst-20775	40	58	s3)142×96	s3)142×96	NOUN
ajst-20775	40	59	avgpool	avgpool	NOUN
ajst-20775	40	60	96	96	NUM
ajst-20775	40	61	1	1	NUM
ajst-20775	40	62	(	(	PUNCT
ajst-20775	40	63	s4)72×144	s4)72×144	SYM
ajst-20775	40	64	avgpool	avgpool	NOUN
ajst-20775	40	65	144	144	NUM
ajst-20775	40	66	1	1	NUM
ajst-20775	40	67	(	(	PUNCT
ajst-20775	40	68	s5)1×1×128	s5)1×1×128	NOUN
ajst-20775	40	69	-128	-128	PROPN
ajst-20775	40	70	s1,s2,s3,s4,s5	s1,s2,s3,s4,s5	PROPN
ajst-20775	40	71	full	full	ADJ
ajst-20775	40	72	connection	connection	NOUN
ajst-20775	40	73	196	196	NUM
ajst-20775	40	74	1	1	NUM
ajst-20775	40	75	2.2	2.2	NUM
ajst-20775	40	76	.	.	PUNCT
ajst-20775	41	1	fatigue	fatigue	NOUN
ajst-20775	41	2	feature	feature	NOUN
ajst-20775	41	3	parameter	parameter	NOUN
ajst-20775	41	4	extraction	extraction	NOUN
ajst-20775	41	5	based	base	VERB
ajst-20775	41	6	on	on	ADP
ajst-20775	41	7	the	the	DET
ajst-20775	41	8	detection	detection	NOUN
ajst-20775	41	9	of	of	ADP
ajst-20775	41	10	98	98	NUM
ajst-20775	41	11	landmark	landmark	NOUN
ajst-20775	41	12	,	,	PUNCT
ajst-20775	41	13	12	12	NUM
ajst-20775	41	14	points	point	NOUN
ajst-20775	41	15	are	be	AUX
ajst-20775	41	16	selected	select	VERB
ajst-20775	41	17	to	to	PART
ajst-20775	41	18	extract	extract	VERB
ajst-20775	41	19	eye	eye	NOUN
ajst-20775	41	20	fatigue	fatigue	NOUN
ajst-20775	41	21	feature	feature	NOUN
ajst-20775	41	22	parameters	parameter	NOUN
ajst-20775	41	23	.	.	PUNCT
ajst-20775	42	1	the	the	DET
ajst-20775	42	2	formulas	formula	NOUN
ajst-20775	42	3	for	for	ADP
ajst-20775	42	4	calculating	calculate	VERB
ajst-20775	42	5	fatigue	fatigue	NOUN
ajst-20775	42	6	feature	feature	NOUN
ajst-20775	42	7	parameters	parameter	NOUN
ajst-20775	42	8	of	of	ADP
ajst-20775	42	9	the	the	DET
ajst-20775	42	10	left	left	ADJ
ajst-20775	42	11	and	and	CCONJ
ajst-20775	42	12	right	right	ADJ
ajst-20775	42	13	eyes	eye	NOUN
ajst-20775	42	14	are	be	AUX
ajst-20775	42	15	shown	show	VERB
ajst-20775	42	16	in	in	ADP
ajst-20775	42	17	equations	equation	NOUN
ajst-20775	42	18	(	(	PUNCT
ajst-20775	42	19	2	2	NUM
ajst-20775	42	20	)	)	PUNCT
ajst-20775	42	21	and	and	CCONJ
ajst-20775	42	22	(	(	PUNCT
ajst-20775	42	23	3	3	NUM
ajst-20775	42	24	)	)	PUNCT
ajst-20775	42	25	.	.	PUNCT
ajst-20775	43	1	this	this	DET
ajst-20775	43	2	paper	paper	NOUN
ajst-20775	43	3	employs	employ	VERB
ajst-20775	43	4	the	the	DET
ajst-20775	43	5	mouth	mouth	NOUN
ajst-20775	43	6	aspect	aspect	NOUN
ajst-20775	43	7	ratio	ratio	NOUN
ajst-20775	43	8	(	(	PUNCT
ajst-20775	43	9	mar	mar	PROPN
ajst-20775	43	10	)	)	PUNCT
ajst-20775	43	11	as	as	ADP
ajst-20775	43	12	a	a	DET
ajst-20775	43	13	fatigue	fatigue	NOUN
ajst-20775	43	14	feature	feature	NOUN
ajst-20775	43	15	parameter	parameter	NOUN
ajst-20775	43	16	,	,	PUNCT
ajst-20775	43	17	with	with	ADP
ajst-20775	43	18	its	its	PRON
ajst-20775	43	19	calculation	calculation	NOUN
ajst-20775	43	20	formula	formula	NOUN
ajst-20775	43	21	presented	present	VERB
ajst-20775	43	22	as	as	SCONJ
ajst-20775	43	23	shown	show	VERB
ajst-20775	43	24	in	in	ADP
ajst-20775	43	25	equation	equation	NOUN
ajst-20775	43	26	(	(	PUNCT
ajst-20775	43	27	4	4	NUM
ajst-20775	43	28	)	)	PUNCT
ajst-20775	43	29	.	.	PUNCT
ajst-20775	44	1	69	69	NUM
ajst-20775	44	2	75	75	NUM
ajst-20775	44	3	71	71	NUM
ajst-20775	44	4	73	73	NUM
ajst-20775	44	5	68	68	NUM
ajst-20775	44	6	72	72	NUM
ajst-20775	45	1	|	|	ADV
ajst-20775	46	1	|	|	ADV
ajst-20775	46	2	|	|	ADV
ajst-20775	46	3	|	|	ADV
ajst-20775	46	4	e	e	NOUN
ajst-20775	46	5	2	2	NUM
ajst-20775	46	6	|	|	ADV
ajst-20775	46	7	|left	|left	VERB
ajst-20775	46	8	y	y	PROPN
ajst-20775	46	9	y	y	PROPN
ajst-20775	46	10	y	y	PROPN
ajst-20775	46	11	y	y	PROPN
ajst-20775	46	12	ar	ar	PROPN
ajst-20775	46	13	x	x	PROPN
ajst-20775	46	14	y	y	PROPN
ajst-20775	46	15			PROPN
ajst-20775	46	16			VERB
ajst-20775	46	17			PROPN
ajst-20775	46	18			PRON
ajst-20775	46	19			NOUN
ajst-20775	46	20	(	(	PUNCT
ajst-20775	46	21	2	2	NUM
ajst-20775	46	22	)	)	PUNCT
ajst-20775	46	23	61	61	NUM
ajst-20775	46	24	67	67	NUM
ajst-20775	46	25	63	63	NUM
ajst-20775	46	26	65	65	NUM
ajst-20775	46	27	60	60	NUM
ajst-20775	46	28	64	64	NUM
ajst-20775	46	29	|	|	ADV
ajst-20775	47	1	|	|	ADV
ajst-20775	47	2	|	|	ADV
ajst-20775	47	3	|	|	ADV
ajst-20775	47	4	e	e	NOUN
ajst-20775	47	5	2	2	NUM
ajst-20775	47	6	|	|	ADV
ajst-20775	47	7	|right	|right	VERB
ajst-20775	47	8	y	y	PROPN
ajst-20775	47	9	y	y	PROPN
ajst-20775	47	10	y	y	PROPN
ajst-20775	47	11	y	y	PROPN
ajst-20775	47	12	ar	ar	PROPN
ajst-20775	47	13	x	x	PROPN
ajst-20775	47	14	y	y	PROPN
ajst-20775	47	15			PROPN
ajst-20775	47	16			VERB
ajst-20775	47	17			PROPN
ajst-20775	47	18			PRON
ajst-20775	47	19			NOUN
ajst-20775	47	20	(	(	PUNCT
ajst-20775	47	21	3	3	NUM
ajst-20775	47	22	)	)	PUNCT
ajst-20775	47	23	78	78	NUM
ajst-20775	47	24	80	80	NUM
ajst-20775	47	25	86	86	NUM
ajst-20775	47	26	84	84	NUM
ajst-20775	47	27	76	76	NUM
ajst-20775	47	28	82	82	NUM
ajst-20775	48	1	|	|	ADV
ajst-20775	48	2	|	|	ADV
ajst-20775	48	3	2	2	NUM
ajst-20775	49	1	|	|	ADV
ajst-20775	49	2	|	|	ADV
ajst-20775	49	3	y	y	PROPN
ajst-20775	49	4	y	y	PROPN
ajst-20775	49	5	y	y	PROPN
ajst-20775	49	6	yh	yh	NOUN
ajst-20775	49	7	mar	mar	PROPN
ajst-20775	49	8	w	w	PROPN
ajst-20775	49	9	x	x	PUNCT
ajst-20775	49	10	x	x	X
ajst-20775	49	11			X
ajst-20775	49	12			PROPN
ajst-20775	49	13			PROPN
ajst-20775	49	14			PROPN
ajst-20775	49	15			PROPN
ajst-20775	49	16			NOUN
ajst-20775	49	17	(	(	PUNCT
ajst-20775	49	18	4	4	NUM
ajst-20775	49	19	)	)	PUNCT
ajst-20775	49	20	this	this	DET
ajst-20775	49	21	paper	paper	NOUN
ajst-20775	49	22	introduces	introduce	NOUN
ajst-20775	49	23	head	head	NOUN
ajst-20775	49	24	euler	euler	NOUN
ajst-20775	49	25	angles	angle	NOUN
ajst-20775	49	26	,	,	PUNCT
ajst-20775	49	27	including	include	VERB
ajst-20775	49	28	pitch	pitch	NOUN
ajst-20775	49	29	,	,	PUNCT
ajst-20775	49	30	yaw	yaw	NOUN
ajst-20775	49	31	,	,	PUNCT
ajst-20775	49	32	and	and	CCONJ
ajst-20775	49	33	roll	roll	NOUN
ajst-20775	49	34	,	,	PUNCT
ajst-20775	49	35	as	as	ADP
ajst-20775	49	36	key	key	ADJ
ajst-20775	49	37	feature	feature	NOUN
ajst-20775	49	38	parameters	parameter	NOUN
ajst-20775	49	39	for	for	ADP
ajst-20775	49	40	fatigue	fatigue	NOUN
ajst-20775	49	41	detection	detection	NOUN
ajst-20775	49	42	.	.	PUNCT
ajst-20775	50	1	the	the	DET
ajst-20775	50	2	calculation	calculation	NOUN
ajst-20775	50	3	method	method	NOUN
ajst-20775	50	4	for	for	ADP
ajst-20775	50	5	head	head	NOUN
ajst-20775	50	6	euler	euler	NOUN
ajst-20775	50	7	angles	angle	NOUN
ajst-20775	50	8	is	be	AUX
ajst-20775	50	9	as	as	SCONJ
ajst-20775	50	10	follows	follow	VERB
ajst-20775	50	11	:	:	PUNCT
ajst-20775	50	12	(	(	PUNCT
ajst-20775	50	13	1	1	X
ajst-20775	50	14	)	)	PUNCT
ajst-20775	50	15	utilize	utilize	VERB
ajst-20775	50	16	a	a	DET
ajst-20775	50	17	2d	2d	NUM
ajst-20775	50	18	facial	facial	ADJ
ajst-20775	50	19	landmark	landmark	NOUN
ajst-20775	50	20	detection	detection	NOUN
ajst-20775	50	21	network	network	NOUN
ajst-20775	50	22	for	for	ADP
ajst-20775	50	23	facial	facial	ADJ
ajst-20775	50	24	landmarks	landmark	NOUN
ajst-20775	50	25	;	;	PUNCT
ajst-20775	50	26	(	(	PUNCT
ajst-20775	50	27	2	2	X
ajst-20775	50	28	)	)	PUNCT
ajst-20775	50	29	match	match	VERB
ajst-20775	50	30	a	a	DET
ajst-20775	50	31	3d	3d	NUM
ajst-20775	50	32	face	face	NOUN
ajst-20775	50	33	model	model	NOUN
ajst-20775	50	34	;	;	PUNCT
ajst-20775	50	35	(	(	PUNCT
ajst-20775	50	36	3	3	X
ajst-20775	50	37	)	)	PUNCT
ajst-20775	50	38	solve	solve	NOUN
ajst-20775	50	39	for	for	ADP
ajst-20775	50	40	the	the	DET
ajst-20775	50	41	transformation	transformation	NOUN
ajst-20775	50	42	relationship	relationship	NOUN
ajst-20775	50	43	between	between	ADP
ajst-20775	50	44	3d	3d	PROPN
ajst-20775	50	45	points	point	NOUN
ajst-20775	50	46	and	and	CCONJ
ajst-20775	50	47	corresponding	correspond	VERB
ajst-20775	50	48	2d	2d	NUM
ajst-20775	50	49	points	point	NOUN
ajst-20775	50	50	;	;	PUNCT
ajst-20775	50	51	(	(	PUNCT
ajst-20775	50	52	4	4	X
ajst-20775	50	53	)	)	PUNCT
ajst-20775	50	54	calculate	calculate	NOUN
ajst-20775	50	55	euler	euler	NOUN
ajst-20775	50	56	angles	angle	NOUN
ajst-20775	50	57	based	base	VERB
ajst-20775	50	58	on	on	ADP
ajst-20775	50	59	the	the	DET
ajst-20775	50	60	rotation	rotation	NOUN
ajst-20775	50	61	matrix	matrix	NOUN
ajst-20775	50	62	.	.	PUNCT
ajst-20775	51	1	the	the	DET
ajst-20775	51	2	transformation	transformation	NOUN
ajst-20775	51	3	formula	formula	NOUN
ajst-20775	51	4	for	for	ADP
ajst-20775	51	5	euler	euler	NOUN
ajst-20775	51	6	angles	angle	NOUN
ajst-20775	51	7	is	be	AUX
ajst-20775	51	8	as	as	ADP
ajst-20775	51	9	equation(5	equation(5	ADJ
ajst-20775	51	10	)	)	PUNCT
ajst-20775	51	11	.	.	PUNCT
ajst-20775	52	1	where	where	SCONJ
ajst-20775	52	2	,	,	PUNCT
ajst-20775	52	3	,	,	PUNCT
ajst-20775	52	4			X
ajst-20775	52	5			NOUN
ajst-20775	52	6			PROPN
ajst-20775	52	7	represent	represent	VERB
ajst-20775	52	8	pitch	pitch	NOUN
ajst-20775	52	9	,	,	PUNCT
ajst-20775	52	10	roll	roll	NOUN
ajst-20775	52	11	and	and	CCONJ
ajst-20775	52	12	yaw	yaw	NOUN
ajst-20775	52	13	angles	angle	NOUN
ajst-20775	52	14	respectively	respectively	ADV
ajst-20775	52	15	.	.	PUNCT
ajst-20775	53	1	21	21	NUM
ajst-20775	53	2	21	21	NUM
ajst-20775	53	3	2	2	NUM
ajst-20775	53	4	2	2	NUM
ajst-20775	53	5	20	20	NUM
ajst-20775	53	6	21	21	NUM
ajst-20775	53	7	22	22	NUM
ajst-20775	53	8	10	10	NUM
ajst-20775	53	9	00	00	NUM
ajst-20775	53	10	tan	tan	PROPN
ajst-20775	53	11	2	2	NUM
ajst-20775	53	12	(	(	PUNCT
ajst-20775	53	13	,	,	PUNCT
ajst-20775	53	14	)	)	PUNCT
ajst-20775	53	15	tan	tan	NOUN
ajst-20775	53	16	2	2	NUM
ajst-20775	53	17	(	(	PUNCT
ajst-20775	53	18	,	,	PUNCT
ajst-20775	53	19	)	)	PUNCT
ajst-20775	53	20	tan	tan	NOUN
ajst-20775	53	21	2	2	NUM
ajst-20775	53	22	(	(	PUNCT
ajst-20775	53	23	,	,	PUNCT
ajst-20775	53	24	)	)	PUNCT
ajst-20775	53	25	a	a	DET
ajst-20775	53	26	r	r	NOUN
ajst-20775	53	27	r	r	NOUN
ajst-20775	53	28	a	a	DET
ajst-20775	53	29	r	r	NOUN
ajst-20775	53	30	r	r	NOUN
ajst-20775	53	31	r	r	NOUN
ajst-20775	53	32	a	a	DET
ajst-20775	53	33	r	r	NOUN
ajst-20775	53	34	r	r	NOUN
ajst-20775	53	35			NOUN
ajst-20775	53	36			NOUN
ajst-20775	53	37			PROPN
ajst-20775	53	38			NOUN
ajst-20775	53	39			PROPN
ajst-20775	53	40			PROPN
ajst-20775	53	41			PROPN
ajst-20775	53	42			NOUN
ajst-20775	53	43			PRON
ajst-20775	53	44			NOUN
ajst-20775	53	45	(	(	PUNCT
ajst-20775	53	46	5	5	NUM
ajst-20775	53	47	)	)	PUNCT
ajst-20775	53	48	in	in	ADP
ajst-20775	53	49	fatigue	fatigue	NOUN
ajst-20775	53	50	driving	driving	NOUN
ajst-20775	53	51	detection	detection	NOUN
ajst-20775	53	52	,	,	PUNCT
ajst-20775	53	53	we	we	PRON
ajst-20775	53	54	extract	extract	VERB
ajst-20775	53	55	a	a	DET
ajst-20775	53	56	series	series	NOUN
ajst-20775	53	57	of	of	ADP
ajst-20775	53	58	fatiguerelated	fatiguerelate	VERB
ajst-20775	53	59	feature	feature	NOUN
ajst-20775	53	60	parameters	parameter	NOUN
ajst-20775	53	61	by	by	ADP
ajst-20775	53	62	analyzing	analyze	VERB
ajst-20775	53	63	facial	facial	ADJ
ajst-20775	53	64	landmark	landmark	NOUN
ajst-20775	53	65	information	information	NOUN
ajst-20775	53	66	in	in	ADP
ajst-20775	53	67	each	each	DET
ajst-20775	53	68	frame	frame	NOUN
ajst-20775	53	69	of	of	ADP
ajst-20775	53	70	the	the	DET
ajst-20775	53	71	video	video	NOUN
ajst-20775	53	72	.	.	PUNCT
ajst-20775	54	1	we	we	PRON
ajst-20775	54	2	use	use	VERB
ajst-20775	54	3	equation	equation	NOUN
ajst-20775	54	4	(	(	PUNCT
ajst-20775	54	5	7	7	NUM
ajst-20775	54	6	)	)	PUNCT
ajst-20775	54	7	to	to	PART
ajst-20775	54	8	represent	represent	VERB
ajst-20775	54	9	the	the	DET
ajst-20775	54	10	matrix	matrix	NOUN
ajst-20775	54	11	of	of	ADP
ajst-20775	54	12	fatigue	fatigue	NOUN
ajst-20775	54	13	feature	feature	NOUN
ajst-20775	54	14	parameters	parameter	NOUN
ajst-20775	54	15	for	for	ADP
ajst-20775	54	16	this	this	DET
ajst-20775	54	17	multivariate	multivariate	NOUN
ajst-20775	54	18	time	time	NOUN
ajst-20775	54	19	series	series	PROPN
ajst-20775	54	20	.	.	PUNCT
ajst-20775	55	1	39	39	NUM
ajst-20775	55	2	1	1	NUM
ajst-20775	55	3	2	2	NUM
ajst-20775	55	4	.	.	PUNCT
ajst-20775	55	5	.	.	PUNCT
ajst-20775	55	6	.	.	PUNCT
ajst-20775	55	7	.	.	PUNCT
ajst-20775	55	8	.	.	PUNCT
ajst-20775	55	9	.	.	PUNCT
ajst-20775	55	10	.	.	PUNCT
ajst-20775	55	11	.	.	PUNCT
ajst-20775	55	12	.	.	PUNCT
ajst-20775	55	13	.	.	PUNCT
ajst-20775	55	14	.	.	PUNCT
ajst-20775	55	15	.	.	PUNCT
ajst-20775	55	16	.	.	PUNCT
ajst-20775	55	17	.	.	PUNCT
ajst-20775	55	18	.	.	PUNCT
ajst-20775	55	19	.	.	PUNCT
ajst-20775	55	20	.	.	PUNCT
ajst-20775	55	21	.	.	PUNCT
ajst-20775	55	22	.	.	PUNCT
ajst-20775	55	23	.	.	PUNCT
ajst-20775	55	24	.	.	PUNCT
ajst-20775	56	1	left	leave	VERB
ajst-20775	56	2	right	right	ADV
ajst-20775	56	3	left	left	ADJ
ajst-20775	56	4	right	right	ADV
ajst-20775	56	5	n	n	PRON
ajst-20775	56	6	left	leave	VERB
ajst-20775	56	7	right	right	NOUN
ajst-20775	57	1	t	t	PROPN
ajst-20775	57	2	ear	ear	NOUN
ajst-20775	57	3	ear	ear	NOUN
ajst-20775	57	4	mar	mar	PROPN
ajst-20775	57	5	t	t	PROPN
ajst-20775	57	6	ear	ear	NOUN
ajst-20775	57	7	ear	ear	NOUN
ajst-20775	57	8	mar	mar	PROPN
ajst-20775	57	9	x	x	PROPN
ajst-20775	57	10	t	t	PROPN
ajst-20775	57	11	ear	ear	NOUN
ajst-20775	57	12	ear	ear	NOUN
ajst-20775	57	13	mar	mar	PROPN
ajst-20775	57	14			PROPN
ajst-20775	57	15			X
ajst-20775	58	1			ADJ
ajst-20775	58	2			NOUN
ajst-20775	58	3			X
ajst-20775	58	4			ADJ
ajst-20775	58	5			NOUN
ajst-20775	58	6			NOUN
ajst-20775	58	7			PROPN
ajst-20775	58	8			PROPN
ajst-20775	58	9			ADJ
ajst-20775	58	10			NOUN
ajst-20775	58	11			PROPN
ajst-20775	58	12			NOUN
ajst-20775	58	13			NOUN
ajst-20775	58	14			NOUN
ajst-20775	58	15			NOUN
ajst-20775	58	16			NUM
ajst-20775	58	17			NOUN
ajst-20775	58	18			NOUN
ajst-20775	58	19			NOUN
ajst-20775	58	20			NOUN
ajst-20775	58	21			NOUN
ajst-20775	58	22			NOUN
ajst-20775	58	23			NOUN
ajst-20775	58	24			NOUN
ajst-20775	58	25			NOUN
ajst-20775	58	26			PROPN
ajst-20775	58	27			PROPN
ajst-20775	58	28	(	(	PUNCT
ajst-20775	58	29	7	7	NUM
ajst-20775	58	30	)	)	PUNCT
ajst-20775	58	31	2.3	2.3	NUM
ajst-20775	58	32	.	.	PUNCT
ajst-20775	59	1	fatigue	fatigue	NOUN
ajst-20775	59	2	driving	drive	VERB
ajst-20775	59	3	discrimination	discrimination	NOUN
ajst-20775	59	4	model	model	NOUN
ajst-20775	59	5	this	this	DET
ajst-20775	59	6	paper	paper	NOUN
ajst-20775	59	7	selects	select	VERB
ajst-20775	59	8	the	the	DET
ajst-20775	59	9	encoder	encoder	NOUN
ajst-20775	59	10	part	part	NOUN
ajst-20775	59	11	of	of	ADP
ajst-20775	59	12	the	the	DET
ajst-20775	59	13	transformer[11	transformer[11	NOUN
ajst-20775	59	14	]	]	PUNCT
ajst-20775	59	15	to	to	PART
ajst-20775	59	16	cater	cater	VERB
ajst-20775	59	17	to	to	ADP
ajst-20775	59	18	the	the	DET
ajst-20775	59	19	needs	need	NOUN
ajst-20775	59	20	of	of	ADP
ajst-20775	59	21	fatigue	fatigue	NOUN
ajst-20775	59	22	driving	drive	VERB
ajst-20775	59	23	discrimination	discrimination	NOUN
ajst-20775	59	24	.	.	PUNCT
ajst-20775	60	1	initially	initially	ADV
ajst-20775	60	2	,	,	PUNCT
ajst-20775	60	3	the	the	DET
ajst-20775	60	4	fatigue	fatigue	NOUN
ajst-20775	60	5	feature	feature	NOUN
ajst-20775	60	6	parameter	parameter	NOUN
ajst-20775	60	7	matrix	matrix	NOUN
ajst-20775	60	8	undergoes	undergo	VERB
ajst-20775	60	9	a	a	DET
ajst-20775	60	10	linear	linear	ADJ
ajst-20775	60	11	transformation	transformation	NOUN
ajst-20775	60	12	to	to	PART
ajst-20775	60	13	convert	convert	VERB
ajst-20775	60	14	it	it	PRON
ajst-20775	60	15	into	into	ADP
ajst-20775	60	16	an	an	DET
ajst-20775	60	17	embedding	embed	VERB
ajst-20775	60	18	sequence	sequence	NOUN
ajst-20775	60	19	,	,	PUNCT
ajst-20775	60	20	which	which	PRON
ajst-20775	60	21	is	be	AUX
ajst-20775	60	22	then	then	ADV
ajst-20775	60	23	fed	feed	VERB
ajst-20775	60	24	into	into	ADP
ajst-20775	60	25	multiple	multiple	ADJ
ajst-20775	60	26	layers	layer	NOUN
ajst-20775	60	27	of	of	ADP
ajst-20775	60	28	the	the	DET
ajst-20775	60	29	transformer	transformer	NOUN
ajst-20775	60	30	encoder	encoder	NOUN
ajst-20775	60	31	as	as	ADP
ajst-20775	60	32	input	input	NOUN
ajst-20775	60	33	.	.	PUNCT
ajst-20775	61	1	for	for	ADP
ajst-20775	61	2	each	each	DET
ajst-20775	61	3	fatigue	fatigue	NOUN
ajst-20775	61	4	feature	feature	NOUN
ajst-20775	61	5	parameter	parameter	NOUN
ajst-20775	61	6	sample	sample	PROPN
ajst-20775	61	7	n	n	PROPN
ajst-20775	61	8	mx	mx	PROPN
ajst-20775	61	9			PROPN
ajst-20775	61	10	,	,	PUNCT
ajst-20775	61	11	which	which	PRON
ajst-20775	61	12	is	be	AUX
ajst-20775	61	13	a	a	DET
ajst-20775	61	14	multivariate	multivariate	ADJ
ajst-20775	61	15	temporal	temporal	ADJ
ajst-20775	61	16	sequence	sequence	NOUN
ajst-20775	61	17	of	of	ADP
ajst-20775	61	18	length	length	NOUN
ajst-20775	61	19	n	n	NOUN
ajst-20775	61	20	and	and	CCONJ
ajst-20775	61	21	number	number	NOUN
ajst-20775	61	22	of	of	ADP
ajst-20775	61	23	variables	variable	NOUN
ajst-20775	61	24	m	m	VERB
ajst-20775	61	25	(	(	PUNCT
ajst-20775	61	26	7	7	NUM
ajst-20775	61	27	m	m	NOUN
ajst-20775	61	28			ADJ
ajst-20775	61	29	in	in	ADP
ajst-20775	61	30	this	this	DET
ajst-20775	61	31	paper	paper	NOUN
ajst-20775	61	32	)	)	PUNCT
ajst-20775	61	33	,	,	PUNCT
ajst-20775	61	34	constitutes	constitute	VERB
ajst-20775	61	35	a	a	DET
ajst-20775	61	36	multivariate	multivariate	ADJ
ajst-20775	61	37	temporal	temporal	ADJ
ajst-20775	61	38	sequence	sequence	NOUN
ajst-20775	61	39	feature	feature	NOUN
ajst-20775	61	40	vector	vector	PROPN
ajst-20775	61	41			PROPN
ajst-20775	61	42	1	1	NOUN
ajst-20775	61	43	2	2	NUM
ajst-20775	61	44	:	:	PUNCT
ajst-20775	61	45	,	,	PUNCT
ajst-20775	61	46	,	,	PUNCT
ajst-20775	61	47	...	...	PUNCT
ajst-20775	61	48	,	,	PUNCT
ajst-20775	61	49	m	m	VERB
ajst-20775	61	50	n	n	PRON
ajst-20775	61	51	m	m	PROPN
ajst-20775	61	52	t	t	NOUN
ajst-20775	61	53	nx	nx	NOUN
ajst-20775	61	54	x	x	SYM
ajst-20775	61	55	x	x	PUNCT
ajst-20775	61	56	x	x	SYM
ajst-20775	61	57	x	x	PROPN
ajst-20775	61	58			NOUN
ajst-20775	61	59			NOUN
ajst-20775	61	60			PUNCT
ajst-20775	61	61	.the	.the	DET
ajst-20775	61	62	original	original	ADJ
ajst-20775	61	63	feature	feature	NOUN
ajst-20775	61	64	vector	vector	NOUN
ajst-20775	61	65	tx	tx	PROPN
ajst-20775	61	66	is	be	AUX
ajst-20775	61	67	first	first	ADV
ajst-20775	61	68	normalized	normalize	VERB
ajst-20775	61	69	,	,	PUNCT
ajst-20775	61	70	and	and	CCONJ
ajst-20775	61	71	then	then	ADV
ajst-20775	61	72	linearly	linearly	ADV
ajst-20775	61	73	projected	project	VERB
ajst-20775	61	74	into	into	ADP
ajst-20775	61	75	the	the	DET
ajst-20775	61	76	$	$	SYM
ajst-20775	61	77	d$	d$	NOUN
ajst-20775	61	78	dimensional	dimensional	ADJ
ajst-20775	61	79	vector	vector	NOUN
ajst-20775	61	80	space	space	NOUN
ajst-20775	61	81	,	,	PUNCT
ajst-20775	61	82	with	with	ADP
ajst-20775	61	83	the	the	DET
ajst-20775	61	84	linear	linear	ADJ
ajst-20775	61	85	transformation	transformation	NOUN
ajst-20775	61	86	formula	formula	NOUN
ajst-20775	61	87	shown	show	VERB
ajst-20775	61	88	in	in	ADP
ajst-20775	61	89	equation	equation	NOUN
ajst-20775	61	90	(	(	PUNCT
ajst-20775	61	91	8)	8)	NUM
ajst-20775	61	92	.	.	PUNCT
ajst-20775	61	93	where	where	SCONJ
ajst-20775	61	94	pw	pw	PROPN
ajst-20775	61	95	d	d	X
ajst-20775	61	96	m	m	PROPN
ajst-20775	61	97	,	,	PUNCT
ajst-20775	61	98	db	db	AUX
ajst-20775	61	99	p	p	ADV
ajst-20775	61	100	are	be	AUX
ajst-20775	61	101	parameters	parameter	NOUN
ajst-20775	61	102	that	that	PRON
ajst-20775	61	103	can	can	AUX
ajst-20775	61	104	be	be	AUX
ajst-20775	61	105	learnt	learn	VERB
ajst-20775	61	106	and	and	CCONJ
ajst-20775	61	107	,	,	PUNCT
ajst-20775	61	108	0,	0,	NUM
ajst-20775	61	109	...	...	PUNCT
ajst-20775	61	110	,d	,d	PUNCT
ajst-20775	61	111	t	t	PROPN
ajst-20775	61	112	n	n	PROPN
ajst-20775	61	113	tu	tu	NUM
ajst-20775	61	114			PUNCT
ajst-20775	61	115	is	be	AUX
ajst-20775	61	116	the	the	DET
ajst-20775	61	117	input	input	ADJ
ajst-20775	61	118	part	part	NOUN
ajst-20775	61	119	of	of	ADP
ajst-20775	61	120	the	the	DET
ajst-20775	61	121	transformer	transformer	ADJ
ajst-20775	61	122	model	model	NOUN
ajst-20775	61	123	enconder	enconder	NOUN
ajst-20775	61	124	.	.	PUNCT
ajst-20775	62	1	since	since	SCONJ
ajst-20775	62	2	the	the	DET
ajst-20775	62	3	t	t	PROPN
ajst-20775	62	4	p	p	PROPN
ajst-20775	62	5	t	t	PROPN
ajst-20775	62	6	pu	pu	PROPN
ajst-20775	62	7	w	w	PROPN
ajst-20775	62	8	x	x	PROPN
ajst-20775	62	9	b	b	PROPN
ajst-20775	62	10			X
ajst-20775	62	11	(	(	PUNCT
ajst-20775	62	12	8)	8)	NUM
ajst-20775	62	13	transformer	transformer	NOUN
ajst-20775	62	14	is	be	AUX
ajst-20775	62	15	an	an	DET
ajst-20775	62	16	architecture	architecture	NOUN
ajst-20775	62	17	that	that	PRON
ajst-20775	62	18	is	be	AUX
ajst-20775	62	19	not	not	PART
ajst-20775	62	20	sensitive	sensitive	ADJ
ajst-20775	62	21	to	to	ADP
ajst-20775	62	22	the	the	DET
ajst-20775	62	23	order	order	NOUN
ajst-20775	62	24	of	of	ADP
ajst-20775	62	25	inputs	input	NOUN
ajst-20775	62	26	,	,	PUNCT
ajst-20775	62	27	in	in	ADP
ajst-20775	62	28	order	order	NOUN
ajst-20775	62	29	to	to	PART
ajst-20775	62	30	incorporate	incorporate	VERB
ajst-20775	62	31	the	the	DET
ajst-20775	62	32	sequential	sequential	ADJ
ajst-20775	62	33	nature	nature	NOUN
ajst-20775	62	34	of	of	ADP
ajst-20775	62	35	temporal	temporal	ADJ
ajst-20775	62	36	sequences	sequence	NOUN
ajst-20775	62	37	,	,	PUNCT
ajst-20775	62	38	in	in	ADP
ajst-20775	62	39	this	this	DET
ajst-20775	62	40	paper	paper	NOUN
ajst-20775	62	41	,	,	PUNCT
ajst-20775	62	42	the	the	DET
ajst-20775	62	43	position	position	NOUN
ajst-20775	62	44	encoding	encode	VERB
ajst-20775	62	45	n	n	PROPN
ajst-20775	62	46	d	d	PROPN
ajst-20775	62	47	posw	posw	NOUN
ajst-20775	62	48			PROPN
ajst-20775	62	49	is	be	AUX
ajst-20775	62	50	added	add	VERB
ajst-20775	62	51	to	to	ADP
ajst-20775	62	52	the	the	DET
ajst-20775	62	53	input	input	NOUN
ajst-20775	62	54	vector	vector	NOUN
ajst-20775	62	55	.	.	PUNCT
ajst-20775	63	1	1	1	NUM
ajst-20775	63	2	[	[	PUNCT
ajst-20775	63	3	,	,	PUNCT
ajst-20775	63	4	...	...	PUNCT
ajst-20775	63	5	,	,	PUNCT
ajst-20775	64	1	]	]	PUNCT
ajst-20775	64	2	n	n	X
ajst-20775	64	3	d	d	X
ajst-20775	64	4	nu	nu	ADJ
ajst-20775	64	5	u	u	PRON
ajst-20775	64	6	u	u	PROPN
ajst-20775	64	7			NOUN
ajst-20775	64	8	represents	represent	VERB
ajst-20775	64	9	the	the	DET
ajst-20775	64	10	linearly	linearly	ADV
ajst-20775	64	11	transformed	transform	VERB
ajst-20775	64	12	feature	feature	NOUN
ajst-20775	64	13	vector	vector	NOUN
ajst-20775	64	14	,	,	PUNCT
ajst-20775	64	15	and	and	CCONJ
ajst-20775	64	16	0z	0z	PROPN
ajst-20775	64	17	represents	represent	VERB
ajst-20775	64	18	the	the	DET
ajst-20775	64	19	resulting	result	VERB
ajst-20775	64	20	time	time	NOUN
ajst-20775	64	21	-	-	PUNCT
ajst-20775	64	22	aware	aware	ADJ
ajst-20775	64	23	feature	feature	NOUN
ajst-20775	64	24	sequence	sequence	NOUN
ajst-20775	64	25	.	.	PUNCT
ajst-20775	65	1	0	0	NUM
ajst-20775	66	1	posz	posz	NOUN
ajst-20775	66	2	u	u	NOUN
ajst-20775	66	3	w	w	VERB
ajst-20775	66	4			PUNCT
ajst-20775	66	5	(	(	PUNCT
ajst-20775	66	6	9	9	NUM
ajst-20775	66	7	)	)	PUNCT
ajst-20775	66	8	to	to	PART
ajst-20775	66	9	capture	capture	VERB
ajst-20775	66	10	intricate	intricate	ADJ
ajst-20775	66	11	interactions	interaction	NOUN
ajst-20775	66	12	among	among	ADP
ajst-20775	66	13	fatigue	fatigue	NOUN
ajst-20775	66	14	feature	feature	NOUN
ajst-20775	66	15	parameters	parameter	NOUN
ajst-20775	66	16	in	in	ADP
ajst-20775	66	17	time	time	NOUN
ajst-20775	66	18	series	series	PROPN
ajst-20775	66	19	,	,	PUNCT
ajst-20775	66	20	the	the	DET
ajst-20775	66	21	input	input	NOUN
ajst-20775	66	22	0z	0z	NOUN
ajst-20775	66	23	undergoes	undergoe	NOUN
ajst-20775	66	24	encoding	encode	VERB
ajst-20775	66	25	through	through	ADP
ajst-20775	66	26	a	a	DET
ajst-20775	66	27	multilayer	multilayer	ADJ
ajst-20775	66	28	transformer	transformer	NOUN
ajst-20775	66	29	.	.	PUNCT
ajst-20775	67	1	0	0	NUM
ajst-20775	67	2	0	0	NUM
ajst-20775	67	3	0	0	NUM
ajst-20775	67	4	(	(	PUNCT
ajst-20775	67	5	,	,	PUNCT
ajst-20775	67	6	,	,	PUNCT
ajst-20775	67	7	)	)	PUNCT
ajst-20775	67	8	max	max	PROPN
ajst-20775	67	9	(	(	PUNCT
ajst-20775	67	10	)	)	PUNCT
ajst-20775	67	11	(	(	PUNCT
ajst-20775	67	12	)	)	PUNCT
ajst-20775	67	13	max	max	PROPN
ajst-20775	67	14	(	(	PUNCT
ajst-20775	67	15	)	)	PUNCT
ajst-20775	68	1	j	j	PROPN
ajst-20775	69	1	j	j	PROPN
ajst-20775	69	2	j	j	PROPN
ajst-20775	70	1	j	j	PROPN
ajst-20775	70	2	t	t	PROPN
ajst-20775	70	3	j	j	PROPN
ajst-20775	71	1	j	j	PROPN
ajst-20775	71	2	j	j	PROPN
ajst-20775	71	3	q	q	PROPN
ajst-20775	72	1	k	k	PROPN
ajst-20775	72	2	t	t	PROPN
ajst-20775	72	3	j	j	PROPN
ajst-20775	72	4	j	j	PROPN
ajst-20775	72	5	v	v	NUM
ajst-20775	72	6	j	j	PROPN
ajst-20775	72	7	head	head	NOUN
ajst-20775	72	8	attention	attention	NOUN
ajst-20775	72	9	q	q	PROPN
ajst-20775	73	1	k	k	NOUN
ajst-20775	73	2	v	v	INTJ
ajst-20775	73	3	q	q	X
ajst-20775	74	1	k	k	PROPN
ajst-20775	74	2	soft	soft	PROPN
ajst-20775	74	3	v	v	PROPN
ajst-20775	74	4	d	d	X
ajst-20775	74	5	z	z	PROPN
ajst-20775	74	6	w	w	PROPN
ajst-20775	74	7	z	z	PROPN
ajst-20775	74	8	w	w	PROPN
ajst-20775	74	9	soft	soft	ADJ
ajst-20775	74	10	z	z	NOUN
ajst-20775	74	11	w	w	PROPN
ajst-20775	75	1	d	d	NOUN
ajst-20775	75	2			NOUN
ajst-20775	76	1			NUM
ajst-20775	76	2			NOUN
ajst-20775	76	3	(	(	PUNCT
ajst-20775	76	4	10	10	NUM
ajst-20775	76	5	)	)	PUNCT
ajst-20775	77	1	where	where	SCONJ
ajst-20775	77	2	0	0	NUM
ajst-20775	77	3	0	0	NUM
ajst-20775	77	4	0	0	NUM
ajst-20775	77	5	,	,	PUNCT
ajst-20775	77	6	,	,	PUNCT
ajst-20775	77	7	q	q	PROPN
ajst-20775	77	8	k	k	X
ajst-20775	77	9	v	v	X
ajst-20775	77	10	j	j	PROPN
ajst-20775	77	11	j	j	PROPN
ajst-20775	77	12	j	j	PROPN
ajst-20775	77	13	j	j	PROPN
ajst-20775	77	14	j	j	PROPN
ajst-20775	77	15	jq	jq	PROPN
ajst-20775	77	16	z	z	PROPN
ajst-20775	77	17	w	w	PROPN
ajst-20775	77	18	k	k	PROPN
ajst-20775	77	19	z	z	PROPN
ajst-20775	77	20	w	w	PROPN
ajst-20775	77	21	v	v	PROPN
ajst-20775	77	22	z	z	NOUN
ajst-20775	77	23	w	w	VERB
ajst-20775	77	24			PROPN
ajst-20775	78	1			PROPN
ajst-20775	78	2	and	and	CCONJ
ajst-20775	78	3	w	w	PROPN
ajst-20775	78	4	represent	represent	VERB
ajst-20775	78	5	learnable	learnable	ADJ
ajst-20775	78	6	linear	linear	PROPN
ajst-20775	78	7	projection	projection	NOUN
ajst-20775	78	8	parameters	parameter	NOUN
ajst-20775	78	9	.	.	PUNCT
ajst-20775	79	1	specifically	specifically	ADV
ajst-20775	79	2	it	it	PRON
ajst-20775	79	3	is	be	AUX
ajst-20775	79	4	the	the	DET
ajst-20775	79	5	projection	projection	NOUN
ajst-20775	79	6	of	of	ADP
ajst-20775	79	7	0z	0z	NUM
ajst-20775	79	8	into	into	ADP
ajst-20775	79	9	a	a	DET
ajst-20775	79	10	different	different	ADJ
ajst-20775	79	11	subspace	subspace	NOUN
ajst-20775	79	12	of	of	ADP
ajst-20775	79	13	hn	hn	PROPN
ajst-20775	79	14	via	via	ADP
ajst-20775	79	15	,	,	PUNCT
ajst-20775	79	16	,	,	PUNCT
ajst-20775	79	17	q	q	PROPN
ajst-20775	79	18	k	k	PROPN
ajst-20775	79	19	v	v	X
ajst-20775	79	20	.the	.the	PRON
ajst-20775	79	21	multi	multi	ADJ
ajst-20775	79	22	-	-	ADJ
ajst-20775	79	23	head	head	ADJ
ajst-20775	79	24	self	self	NOUN
ajst-20775	79	25	-	-	PUNCT
ajst-20775	79	26	attention	attention	NOUN
ajst-20775	79	27	can	can	AUX
ajst-20775	79	28	be	be	AUX
ajst-20775	79	29	expressed	express	VERB
ajst-20775	79	30	as	as	ADP
ajst-20775	79	31	equation(11	equation(11	NOUN
ajst-20775	79	32	)	)	PUNCT
ajst-20775	79	33	.	.	PUNCT
ajst-20775	80	1	hn	hn	PROPN
ajst-20775	80	2	denotes	denote	VERB
ajst-20775	80	3	the	the	DET
ajst-20775	80	4	number	number	NOUN
ajst-20775	80	5	of	of	ADP
ajst-20775	80	6	distinct	distinct	ADJ
ajst-20775	80	7	heads	head	NOUN
ajst-20775	80	8	,	,	PUNCT
ajst-20775	80	9	concat	concat	NOUN
ajst-20775	80	10	represents	represent	VERB
ajst-20775	80	11	the	the	DET
ajst-20775	80	12	splicing	splicing	NOUN
ajst-20775	80	13	operation	operation	NOUN
ajst-20775	80	14	,	,	PUNCT
ajst-20775	80	15	and	and	CCONJ
ajst-20775	80	16	ow	ow	INTJ
ajst-20775	80	17	represents	represent	VERB
ajst-20775	80	18	the	the	DET
ajst-20775	80	19	linear	linear	PROPN
ajst-20775	80	20	projection	projection	NOUN
ajst-20775	80	21	parameters	parameter	NOUN
ajst-20775	80	22	.	.	PUNCT
ajst-20775	81	1	0	0	NUM
ajst-20775	82	1	1	1	NUM
ajst-20775	82	2	(	(	PUNCT
ajst-20775	82	3	)	)	PUNCT
ajst-20775	82	4	(	(	PUNCT
ajst-20775	82	5	,	,	PUNCT
ajst-20775	82	6	...	...	PUNCT
ajst-20775	82	7	,	,	PUNCT
ajst-20775	82	8	)	)	PUNCT
ajst-20775	83	1	h	h	NOUN
ajst-20775	83	2	o	o	NOUN
ajst-20775	83	3	nmsha	nmsha	PROPN
ajst-20775	83	4	z	z	PROPN
ajst-20775	83	5	concat	concat	PROPN
ajst-20775	83	6	head	head	NOUN
ajst-20775	83	7	head	head	NOUN
ajst-20775	83	8	w	w	VERB
ajst-20775	83	9	(	(	PUNCT
ajst-20775	83	10	11	11	NUM
ajst-20775	83	11	)	)	PUNCT
ajst-20775	83	12	each	each	DET
ajst-20775	83	13	transformer	transformer	NOUN
ajst-20775	83	14	's	's	PART
ajst-20775	83	15	enconder	enconder	NOUN
ajst-20775	83	16	contains	contain	VERB
ajst-20775	83	17	n	n	PRON
ajst-20775	83	18	multi	multi	ADJ
ajst-20775	83	19	-	-	ADJ
ajst-20775	83	20	head	head	ADJ
ajst-20775	83	21	selfattention	selfattention	NOUN
ajst-20775	83	22	blocks	block	NOUN
ajst-20775	83	23	.	.	PUNCT
ajst-20775	84	1	the	the	DET
ajst-20775	84	2	standard	standard	ADJ
ajst-20775	84	3	transformer	transformer	NOUN
ajst-20775	84	4	encoder	encoder	NOUN
ajst-20775	84	5	forward	forward	ADJ
ajst-20775	84	6	computation	computation	NOUN
ajst-20775	84	7	is	be	AUX
ajst-20775	84	8	as	as	SCONJ
ajst-20775	84	9	follows	follow	VERB
ajst-20775	84	10	:	:	PUNCT
ajst-20775	84	11			PROPN
ajst-20775	84	12	1	1	NUM
ajst-20775	84	13	1	1	NUM
ajst-20775	84	14	(	(	PUNCT
ajst-20775	84	15	(	(	PUNCT
ajst-20775	84	16	)	)	PUNCT
ajst-20775	84	17	)	)	PUNCT
ajst-20775	85	1	i	i	PRON
ajst-20775	86	1	i	i	PRON
ajst-20775	87	1	iz	iz	INTJ
ajst-20775	87	2	mhsa	mhsa	VERB
ajst-20775	87	3	ln	ln	PROPN
ajst-20775	87	4	z	z	PROPN
ajst-20775	87	5	z	z	NOUN
ajst-20775	87	6			ADJ
ajst-20775	87	7			X
ajst-20775	87	8	(	(	PUNCT
ajst-20775	87	9	12	12	NUM
ajst-20775	87	10	)	)	PUNCT
ajst-20775	87	11			ADJ
ajst-20775	87	12			X
ajst-20775	87	13	(	(	PUNCT
ajst-20775	87	14	(	(	PUNCT
ajst-20775	87	15	)	)	PUNCT
ajst-20775	87	16	)	)	PUNCT
ajst-20775	88	1	i	i	PRON
ajst-20775	88	2	i	i	PRON
ajst-20775	89	1	iz	iz	INTJ
ajst-20775	89	2	mlp	mlp	NOUN
ajst-20775	89	3	ln	ln	PROPN
ajst-20775	89	4	z	z	PROPN
ajst-20775	89	5	z	z	PROPN
ajst-20775	89	6			X
ajst-20775	89	7	(	(	PUNCT
ajst-20775	89	8	13	13	NUM
ajst-20775	89	9	)	)	PUNCT
ajst-20775	89	10	iz	iz	NOUN
ajst-20775	90	1	and	and	CCONJ
ajst-20775	90	2	iz	iz	INTJ
ajst-20775	90	3	denote	denote	VERB
ajst-20775	90	4	the	the	DET
ajst-20775	90	5	output	output	NOUN
ajst-20775	90	6	and	and	CCONJ
ajst-20775	90	7	final	final	ADJ
ajst-20775	90	8	output	output	NOUN
ajst-20775	90	9	of	of	ADP
ajst-20775	90	10	the	the	DET
ajst-20775	90	11	intermediate	intermediate	ADJ
ajst-20775	90	12	layer	layer	NOUN
ajst-20775	90	13	of	of	ADP
ajst-20775	90	14	layer	layer	NOUN
ajst-20775	90	15	i	i	PRON
ajst-20775	90	16	,	,	PUNCT
ajst-20775	90	17	respectively	respectively	ADV
ajst-20775	90	18	.	.	PUNCT
ajst-20775	91	1	mlp	mlp	PROPN
ajst-20775	91	2	composed	compose	VERB
ajst-20775	91	3	of	of	ADP
ajst-20775	91	4	two	two	NUM
ajst-20775	91	5	linear	linear	ADJ
ajst-20775	91	6	feedforward	feedforward	NOUN
ajst-20775	91	7	layers	layer	NOUN
ajst-20775	91	8	and	and	CCONJ
ajst-20775	91	9	a	a	DET
ajst-20775	91	10	gelu	gelu	ADJ
ajst-20775	91	11	non	non	ADJ
ajst-20775	91	12	-	-	ADJ
ajst-20775	91	13	linear	linear	ADJ
ajst-20775	91	14	activation	activation	NOUN
ajst-20775	91	15	function	function	NOUN
ajst-20775	91	16	.	.	PUNCT
ajst-20775	92	1	to	to	PART
ajst-20775	92	2	adapt	adapt	VERB
ajst-20775	92	3	it	it	PRON
ajst-20775	92	4	to	to	ADP
ajst-20775	92	5	the	the	DET
ajst-20775	92	6	classification	classification	NOUN
ajst-20775	92	7	task	task	NOUN
ajst-20775	92	8	of	of	ADP
ajst-20775	92	9	fatigue	fatigue	NOUN
ajst-20775	92	10	driving	drive	VERB
ajst-20775	92	11	detection	detection	NOUN
ajst-20775	92	12	,	,	PUNCT
ajst-20775	92	13	the	the	DET
ajst-20775	92	14	final	final	ADJ
ajst-20775	92	15	output	output	NOUN
ajst-20775	92	16	of	of	ADP
ajst-20775	92	17	the	the	DET
ajst-20775	92	18	transformer	transformer	NOUN
ajst-20775	92	19	encoder	encoder	NOUN
ajst-20775	92	20	model	model	NOUN
ajst-20775	92	21	0	0	NUM
ajst-20775	92	22	nz	nz	PROPN
ajst-20775	92	23	is	be	AUX
ajst-20775	92	24	normalized	normalize	VERB
ajst-20775	92	25	to	to	PART
ajst-20775	92	26	obtain	obtain	VERB
ajst-20775	92	27	the	the	DET
ajst-20775	92	28	output	output	NOUN
ajst-20775	92	29	.	.	PUNCT
ajst-20775	93	1	further	far	ADV
ajst-20775	93	2	,	,	PUNCT
ajst-20775	93	3	the	the	DET
ajst-20775	93	4	model	model	NOUN
ajst-20775	93	5	passes	pass	VERB
ajst-20775	93	6	through	through	ADP
ajst-20775	93	7	fully	fully	ADV
ajst-20775	93	8	connected	connect	VERB
ajst-20775	93	9	layers	layer	NOUN
ajst-20775	93	10	softmax	softmax	NOUN
ajst-20775	93	11	and	and	CCONJ
ajst-20775	93	12	to	to	PART
ajst-20775	93	13	obtain	obtain	VERB
ajst-20775	93	14	the	the	DET
ajst-20775	93	15	final	final	ADJ
ajst-20775	93	16	probability	probability	NOUN
ajst-20775	93	17	of	of	ADP
ajst-20775	93	18	the	the	DET
ajst-20775	93	19	fatigue	fatigue	NOUN
ajst-20775	93	20	state	state	NOUN
ajst-20775	93	21	.	.	PUNCT
ajst-20775	94	1	0	0	PUNCT
ajst-20775	94	2	(	(	PUNCT
ajst-20775	94	3	)	)	PUNCT
ajst-20775	94	4	ny	ny	PROPN
ajst-20775	94	5	ln	ln	PROPN
ajst-20775	94	6	z	z	PROPN
ajst-20775	94	7	(	(	PUNCT
ajst-20775	94	8	14	14	NUM
ajst-20775	94	9	)	)	PUNCT
ajst-20775	94	10	max	max	PROPN
ajst-20775	94	11	(	(	PUNCT
ajst-20775	94	12	)	)	PUNCT
ajst-20775	94	13	y	y	PROPN
ajst-20775	94	14	soft	soft	ADJ
ajst-20775	94	15	y	y	NOUN
ajst-20775	94	16	(	(	PUNCT
ajst-20775	94	17	15	15	NUM
ajst-20775	94	18	)	)	PUNCT
ajst-20775	94	19	where	where	SCONJ
ajst-20775	94	20	y	y	PROPN
ajst-20775	94	21	is	be	AUX
ajst-20775	94	22	the	the	DET
ajst-20775	94	23	output	output	NOUN
ajst-20775	94	24	of	of	ADP
ajst-20775	94	25	the	the	DET
ajst-20775	94	26	multilayer	multilayer	ADJ
ajst-20775	94	27	encoder	encoder	NOUN
ajst-20775	94	28	.	.	PUNCT
ajst-20775	95	1	the	the	DET
ajst-20775	95	2	transformer	transformer	NOUN
ajst-20775	95	3	model	model	NOUN
ajst-20775	95	4	for	for	ADP
ajst-20775	95	5	time	time	NOUN
ajst-20775	95	6	series	series	PROPN
ajst-20775	95	7	can	can	AUX
ajst-20775	95	8	obtain	obtain	VERB
ajst-20775	95	9	the	the	DET
ajst-20775	95	10	correlation	correlation	NOUN
ajst-20775	95	11	between	between	ADP
ajst-20775	95	12	the	the	DET
ajst-20775	95	13	fatigue	fatigue	NOUN
ajst-20775	95	14	feature	feature	NOUN
ajst-20775	95	15	parameters	parameter	NOUN
ajst-20775	95	16	before	before	ADP
ajst-20775	95	17	and	and	CCONJ
ajst-20775	95	18	after	after	ADP
ajst-20775	95	19	each	each	DET
ajst-20775	95	20	frame	frame	NOUN
ajst-20775	95	21	in	in	ADP
ajst-20775	95	22	the	the	DET
ajst-20775	95	23	video	video	NOUN
ajst-20775	95	24	,	,	PUNCT
ajst-20775	95	25	which	which	PRON
ajst-20775	95	26	can	can	AUX
ajst-20775	95	27	better	well	ADV
ajst-20775	95	28	capture	capture	VERB
ajst-20775	95	29	the	the	DET
ajst-20775	95	30	dynamic	dynamic	ADJ
ajst-20775	95	31	changes	change	NOUN
ajst-20775	95	32	of	of	ADP
ajst-20775	95	33	the	the	DET
ajst-20775	95	34	fatigue	fatigue	NOUN
ajst-20775	95	35	state	state	NOUN
ajst-20775	95	36	,	,	PUNCT
ajst-20775	95	37	thus	thus	ADV
ajst-20775	95	38	improving	improve	VERB
ajst-20775	95	39	the	the	DET
ajst-20775	95	40	accuracy	accuracy	NOUN
ajst-20775	95	41	and	and	CCONJ
ajst-20775	95	42	reliability	reliability	NOUN
ajst-20775	95	43	of	of	ADP
ajst-20775	95	44	the	the	DET
ajst-20775	95	45	detection	detection	NOUN
ajst-20775	95	46	.	.	PUNCT
ajst-20775	96	1	3	3	X
ajst-20775	96	2	.	.	X
ajst-20775	96	3	experiment	experiment	NOUN
ajst-20775	96	4	3.1	3.1	NUM
ajst-20775	96	5	.	.	PUNCT
ajst-20775	97	1	dataset	dataset	VERB
ajst-20775	97	2	to	to	PART
ajst-20775	97	3	assess	assess	VERB
ajst-20775	97	4	the	the	DET
ajst-20775	97	5	performance	performance	NOUN
ajst-20775	97	6	of	of	ADP
ajst-20775	97	7	the	the	DET
ajst-20775	97	8	facial	facial	ADJ
ajst-20775	97	9	landmark	landmark	NOUN
ajst-20775	97	10	detection	detection	NOUN
ajst-20775	97	11	algorithm	algorithm	NOUN
ajst-20775	97	12	,	,	PUNCT
ajst-20775	97	13	experiments	experiment	NOUN
ajst-20775	97	14	were	be	AUX
ajst-20775	97	15	conducted	conduct	VERB
ajst-20775	97	16	on	on	ADP
ajst-20775	97	17	the	the	DET
ajst-20775	97	18	wflw	wflw	PROPN
ajst-20775	97	19	dataset	dataset	PROPN
ajst-20775	97	20	.	.	PUNCT
ajst-20775	98	1	the	the	DET
ajst-20775	98	2	nthu	nthu	VERB
ajst-20775	98	3	-	-	PUNCT
ajst-20775	98	4	ddd	ddd	NOUN
ajst-20775	98	5	dataset	dataset	NOUN
ajst-20775	98	6	,	,	PUNCT
ajst-20775	98	7	created	create	VERB
ajst-20775	98	8	by	by	ADP
ajst-20775	98	9	national	national	ADJ
ajst-20775	98	10	tsing	tsing	PROPN
ajst-20775	98	11	hua	hua	PROPN
ajst-20775	98	12	university	university	PROPN
ajst-20775	98	13	,	,	PUNCT
ajst-20775	98	14	aims	aim	VERB
ajst-20775	98	15	to	to	PART
ajst-20775	98	16	simulate	simulate	VERB
ajst-20775	98	17	typical	typical	ADJ
ajst-20775	98	18	scenarios	scenario	NOUN
ajst-20775	98	19	under	under	ADP
ajst-20775	98	20	nearly	nearly	ADV
ajst-20775	98	21	real	real	ADJ
ajst-20775	98	22	driving	driving	NOUN
ajst-20775	98	23	conditions	condition	NOUN
ajst-20775	98	24	.	.	PUNCT
ajst-20775	99	1	to	to	PART
ajst-20775	99	2	construct	construct	VERB
ajst-20775	99	3	temporal	temporal	ADJ
ajst-20775	99	4	sequence	sequence	NOUN
ajst-20775	99	5	samples	sample	NOUN
ajst-20775	99	6	,	,	PUNCT
ajst-20775	99	7	each	each	DET
ajst-20775	99	8	video	video	NOUN
ajst-20775	99	9	was	be	AUX
ajst-20775	99	10	divided	divide	VERB
ajst-20775	99	11	into	into	ADP
ajst-20775	99	12	consecutive	consecutive	ADJ
ajst-20775	99	13	6	6	NUM
ajst-20775	99	14	-	-	PUNCT
ajst-20775	99	15	second	second	NOUN
ajst-20775	99	16	interval	interval	NOUN
ajst-20775	99	17	samples	sample	NOUN
ajst-20775	99	18	,	,	PUNCT
ajst-20775	99	19	with	with	ADP
ajst-20775	99	20	a	a	DET
ajst-20775	99	21	3	3	NUM
ajst-20775	99	22	-	-	PUNCT
ajst-20775	99	23	second	second	NOUN
ajst-20775	99	24	interval	interval	NOUN
ajst-20775	99	25	between	between	ADP
ajst-20775	99	26	each	each	DET
ajst-20775	99	27	sampling	sampling	NOUN
ajst-20775	99	28	,	,	PUNCT
ajst-20775	99	29	ultimately	ultimately	ADV
ajst-20775	99	30	creating	create	VERB
ajst-20775	99	31	a	a	DET
ajst-20775	99	32	new	new	ADJ
ajst-20775	99	33	dataset	dataset	NOUN
ajst-20775	99	34	.	.	PUNCT
ajst-20775	100	1	3.2	3.2	NUM
ajst-20775	100	2	.	.	PUNCT
ajst-20775	100	3	evaluation	evaluation	NOUN
ajst-20775	100	4	metrics	metric	NOUN
ajst-20775	100	5	in	in	ADP
ajst-20775	100	6	order	order	NOUN
ajst-20775	100	7	to	to	PART
ajst-20775	100	8	evaluate	evaluate	VERB
ajst-20775	100	9	the	the	DET
ajst-20775	100	10	performance	performance	NOUN
ajst-20775	100	11	of	of	ADP
ajst-20775	100	12	the	the	DET
ajst-20775	100	13	proposed	propose	VERB
ajst-20775	100	14	landmark	landmark	PROPN
ajst-20775	100	15	detection	detection	NOUN
ajst-20775	100	16	method	method	NOUN
ajst-20775	100	17	,	,	PUNCT
ajst-20775	100	18	normalised	normalise	VERB
ajst-20775	100	19	mean	mean	NOUN
ajst-20775	100	20	error	error	NOUN
ajst-20775	100	21	(	(	PUNCT
ajst-20775	100	22	nme	nme	PROPN
ajst-20775	100	23	)	)	PUNCT
ajst-20775	100	24	is	be	AUX
ajst-20775	100	25	used	use	VERB
ajst-20775	100	26	.	.	PUNCT
ajst-20775	101	1	nme	nme	PROPN
ajst-20775	101	2	is	be	AUX
ajst-20775	101	3	commonly	commonly	ADV
ajst-20775	101	4	used	use	VERB
ajst-20775	101	5	as	as	ADP
ajst-20775	101	6	a	a	DET
ajst-20775	101	7	common	common	ADJ
ajst-20775	101	8	metric	metric	NOUN
ajst-20775	101	9	to	to	PART
ajst-20775	101	10	measure	measure	VERB
ajst-20775	101	11	the	the	DET
ajst-20775	101	12	performance	performance	NOUN
ajst-20775	101	13	of	of	ADP
ajst-20775	101	14	landmark	landmark	NOUN
ajst-20775	101	15	detection	detection	NOUN
ajst-20775	101	16	algorithms	algorithm	NOUN
ajst-20775	101	17	.	.	PUNCT
ajst-20775	102	1	the	the	DET
ajst-20775	102	2	nme	nme	PROPN
ajst-20775	102	3	for	for	ADP
ajst-20775	102	4	each	each	DET
ajst-20775	102	5	image	image	NOUN
ajst-20775	102	6	is	be	AUX
ajst-20775	102	7	defined	define	VERB
ajst-20775	102	8	as	as	ADP
ajst-20775	102	9	(	(	PUNCT
ajst-20775	102	10	16	16	NUM
ajst-20775	102	11	)	)	PUNCT
ajst-20775	102	12	.	.	PUNCT
ajst-20775	103	1	the	the	DET
ajst-20775	103	2	accuracy	accuracy	NOUN
ajst-20775	103	3	of	of	ADP
ajst-20775	103	4	the	the	DET
ajst-20775	103	5	fatigued	fatigued	ADJ
ajst-20775	103	6	driving	drive	VERB
ajst-20775	103	7	discrimination	discrimination	NOUN
ajst-20775	103	8	model	model	NOUN
ajst-20775	103	9	is	be	AUX
ajst-20775	103	10	represented	represent	VERB
ajst-20775	103	11	using	use	VERB
ajst-20775	103	12	40	40	NUM
ajst-20775	103	13	equation	equation	NOUN
ajst-20775	103	14	(	(	PUNCT
ajst-20775	103	15	17	17	NUM
ajst-20775	103	16	)	)	PUNCT
ajst-20775	103	17	.	.	PUNCT
ajst-20775	104	1			NOUN
ajst-20775	105	1			PUNCT
ajst-20775	105	2	2	2	NUM
ajst-20775	105	3	1	1	NUM
ajst-20775	105	4	ˆ1ˆ	ˆ1ˆ	NOUN
ajst-20775	105	5	,	,	PUNCT
ajst-20775	105	6	m	m	AUX
ajst-20775	105	7	i	i	INTJ
ajst-20775	105	8	i	i	PRON
ajst-20775	106	1	i	i	PRON
ajst-20775	106	2	p	p	X
ajst-20775	107	1	p	p	PROPN
ajst-20775	107	2	nme	nme	PROPN
ajst-20775	108	1	p	p	PROPN
ajst-20775	108	2	p	p	PROPN
ajst-20775	108	3	m	m	PROPN
ajst-20775	108	4	d	d	PROPN
ajst-20775	108	5			PROPN
ajst-20775	108	6			PRON
ajst-20775	108	7			X
ajst-20775	108	8	(	(	PUNCT
ajst-20775	108	9	16	16	NUM
ajst-20775	108	10	)	)	PUNCT
ajst-20775	108	11	tp	tp	ADP
ajst-20775	108	12	tn	tn	PROPN
ajst-20775	108	13	accuracy	accuracy	NOUN
ajst-20775	108	14	tp	tp	ADP
ajst-20775	108	15	tn	tn	PROPN
ajst-20775	108	16	fp	fp	PROPN
ajst-20775	108	17	fn	fn	PROPN
ajst-20775	108	18			PROPN
ajst-20775	108	19			PROPN
ajst-20775	108	20			PROPN
ajst-20775	108	21			PUNCT
ajst-20775	108	22			X
ajst-20775	108	23	(	(	PUNCT
ajst-20775	108	24	17	17	NUM
ajst-20775	108	25	)	)	PUNCT
ajst-20775	108	26	3.3	3.3	NUM
ajst-20775	108	27	.	.	PUNCT
ajst-20775	109	1	results	result	VERB
ajst-20775	109	2	analysis	analysis	NOUN
ajst-20775	109	3	landmark	landmark	NOUN
ajst-20775	109	4	detection	detection	NOUN
ajst-20775	109	5	:	:	PUNCT
ajst-20775	109	6	to	to	PART
ajst-20775	109	7	evaluate	evaluate	VERB
ajst-20775	109	8	the	the	DET
ajst-20775	109	9	performance	performance	NOUN
ajst-20775	109	10	of	of	ADP
ajst-20775	109	11	the	the	DET
ajst-20775	109	12	proposed	propose	VERB
ajst-20775	109	13	multi	multi	ADJ
ajst-20775	109	14	-	-	ADJ
ajst-20775	109	15	scale	scale	ADJ
ajst-20775	109	16	fusion	fusion	NOUN
ajst-20775	109	17	facial	facial	ADJ
ajst-20775	109	18	landmark	landmark	NOUN
ajst-20775	109	19	detection	detection	NOUN
ajst-20775	109	20	algorithm	algorithm	NOUN
ajst-20775	109	21	,	,	PUNCT
ajst-20775	109	22	this	this	DET
ajst-20775	109	23	paper	paper	NOUN
ajst-20775	109	24	compares	compare	VERB
ajst-20775	109	25	the	the	DET
ajst-20775	109	26	algorithm	algorithm	NOUN
ajst-20775	109	27	with	with	ADP
ajst-20775	109	28	current	current	ADJ
ajst-20775	109	29	landmark	landmark	NOUN
ajst-20775	109	30	detection	detection	NOUN
ajst-20775	109	31	algorithms	algorithm	NOUN
ajst-20775	109	32	on	on	ADP
ajst-20775	109	33	the	the	DET
ajst-20775	109	34	wflw	wflw	PROPN
ajst-20775	109	35	dataset	dataset	PROPN
ajst-20775	109	36	.	.	PUNCT
ajst-20775	110	1	the	the	DET
ajst-20775	110	2	algorithm	algorithm	NOUN
ajst-20775	110	3	's	's	PART
ajst-20775	110	4	normalized	normalize	VERB
ajst-20775	110	5	mean	mean	NOUN
ajst-20775	110	6	error	error	NOUN
ajst-20775	110	7	(	(	PUNCT
ajst-20775	110	8	nme	nme	NOUN
ajst-20775	110	9	)	)	PUNCT
ajst-20775	110	10	values	value	NOUN
ajst-20775	110	11	,	,	PUNCT
ajst-20775	110	12	model	model	NOUN
ajst-20775	110	13	size	size	NOUN
ajst-20775	110	14	,	,	PUNCT
ajst-20775	110	15	and	and	CCONJ
ajst-20775	110	16	inference	inference	NOUN
ajst-20775	110	17	speed	speed	NOUN
ajst-20775	110	18	were	be	AUX
ajst-20775	110	19	compared	compare	VERB
ajst-20775	110	20	with	with	ADP
ajst-20775	110	21	other	other	ADJ
ajst-20775	110	22	algorithms	algorithm	NOUN
ajst-20775	110	23	,	,	PUNCT
ajst-20775	110	24	with	with	ADP
ajst-20775	110	25	results	result	NOUN
ajst-20775	110	26	presented	present	VERB
ajst-20775	110	27	in	in	ADP
ajst-20775	110	28	table	table	NOUN
ajst-20775	110	29	2	2	NUM
ajst-20775	110	30	.	.	PUNCT
ajst-20775	110	31	table	table	NOUN
ajst-20775	110	32	2	2	NUM
ajst-20775	110	33	.	.	PUNCT
ajst-20775	110	34	landmark	landmark	PROPN
ajst-20775	110	35	detection	detection	PROPN
ajst-20775	110	36	result	result	VERB
ajst-20775	110	37	diagram	diagram	NOUN
ajst-20775	110	38	methods	method	NOUN
ajst-20775	110	39	model	model	NOUN
ajst-20775	110	40	size(mb	size(mb	PROPN
ajst-20775	110	41	)	)	PUNCT
ajst-20775	110	42	inference	inference	NOUN
ajst-20775	110	43	time(ms	time(ms	NOUN
ajst-20775	110	44	)	)	PUNCT
ajst-20775	110	45	nme(%	nme(%	NOUN
ajst-20775	110	46	)	)	PUNCT
ajst-20775	110	47	lab[12	lab[12	NOUN
ajst-20775	110	48	]	]	X
ajst-20775	111	1	50.7	50.7	NUM
ajst-20775	111	2	2600	2600	NUM
ajst-20775	111	3	5.27	5.27	NUM
ajst-20775	111	4	pfld[13	pfld[13	NOUN
ajst-20775	111	5	]	]	X
ajst-20775	111	6	5.0	5.0	NUM
ajst-20775	111	7	5.5	5.5	NUM
ajst-20775	111	8	5.52	5.52	NUM
ajst-20775	111	9	ours	ours	PRON
ajst-20775	111	10	2.71	2.71	NUM
ajst-20775	111	11	2.5	2.5	NUM
ajst-20775	111	12	5.21	5.21	NUM
ajst-20775	111	13	research	research	NOUN
ajst-20775	111	14	[	[	X
ajst-20775	111	15	12	12	NUM
ajst-20775	111	16	]	]	PUNCT
ajst-20775	111	17	achieved	achieve	VERB
ajst-20775	111	18	a	a	DET
ajst-20775	111	19	5.27	5.27	NUM
ajst-20775	111	20	%	%	NOUN
ajst-20775	111	21	normalized	normalize	VERB
ajst-20775	111	22	mean	mean	VERB
ajst-20775	111	23	squared	square	VERB
ajst-20775	111	24	error	error	NOUN
ajst-20775	111	25	on	on	ADP
ajst-20775	111	26	the	the	DET
ajst-20775	111	27	dataset	dataset	NOUN
ajst-20775	111	28	by	by	ADP
ajst-20775	111	29	adopting	adopt	VERB
ajst-20775	111	30	boundary	boundary	ADJ
ajst-20775	111	31	-	-	PUNCT
ajst-20775	111	32	aware	aware	ADJ
ajst-20775	111	33	feature	feature	NOUN
ajst-20775	111	34	extraction	extraction	NOUN
ajst-20775	111	35	,	,	PUNCT
ajst-20775	111	36	structured	structure	VERB
ajst-20775	111	37	output	output	NOUN
ajst-20775	111	38	networks	network	NOUN
ajst-20775	111	39	,	,	PUNCT
ajst-20775	111	40	multi	multi	ADJ
ajst-20775	111	41	-	-	ADJ
ajst-20775	111	42	task	task	ADJ
ajst-20775	111	43	learning	learning	NOUN
ajst-20775	111	44	,	,	PUNCT
ajst-20775	111	45	and	and	CCONJ
ajst-20775	111	46	boundary	boundary	ADJ
ajst-20775	111	47	refinement	refinement	NOUN
ajst-20775	111	48	techniques	technique	NOUN
ajst-20775	111	49	.	.	PUNCT
ajst-20775	112	1	this	this	DET
ajst-20775	112	2	method	method	NOUN
ajst-20775	112	3	demonstrated	demonstrate	VERB
ajst-20775	112	4	good	good	ADJ
ajst-20775	112	5	robustness	robustness	NOUN
ajst-20775	112	6	in	in	ADP
ajst-20775	112	7	handling	handle	VERB
ajst-20775	112	8	situations	situation	NOUN
ajst-20775	112	9	involving	involve	VERB
ajst-20775	112	10	complex	complex	ADJ
ajst-20775	112	11	expressions	expression	NOUN
ajst-20775	112	12	and	and	CCONJ
ajst-20775	112	13	pose	pose	VERB
ajst-20775	112	14	changes	change	NOUN
ajst-20775	112	15	.	.	PUNCT
ajst-20775	113	1	despite	despite	SCONJ
ajst-20775	113	2	its	its	PRON
ajst-20775	113	3	robustness	robustness	NOUN
ajst-20775	113	4	to	to	ADP
ajst-20775	113	5	complex	complex	ADJ
ajst-20775	113	6	expressions	expression	NOUN
ajst-20775	113	7	and	and	CCONJ
ajst-20775	113	8	pose	pose	NOUN
ajst-20775	113	9	changes	change	NOUN
ajst-20775	113	10	,	,	PUNCT
ajst-20775	113	11	the	the	DET
ajst-20775	113	12	model	model	NOUN
ajst-20775	113	13	size	size	NOUN
ajst-20775	113	14	is	be	AUX
ajst-20775	113	15	as	as	ADV
ajst-20775	113	16	large	large	ADJ
ajst-20775	113	17	as	as	ADP
ajst-20775	113	18	50.7	50.7	NUM
ajst-20775	113	19	mb	mb	NOUN
ajst-20775	113	20	,	,	PUNCT
ajst-20775	113	21	and	and	CCONJ
ajst-20775	113	22	the	the	DET
ajst-20775	113	23	inference	inference	NOUN
ajst-20775	113	24	time	time	NOUN
ajst-20775	113	25	reaches	reach	VERB
ajst-20775	113	26	2600ms	2600ms	ADJ
ajst-20775	113	27	.	.	PUNCT
ajst-20775	114	1	research[13	research[13	NOUN
ajst-20775	114	2	]	]	X
ajst-20775	114	3	utilized	utilize	VERB
ajst-20775	114	4	mobilenet	mobilenet	NOUN
ajst-20775	114	5	as	as	ADP
ajst-20775	114	6	the	the	DET
ajst-20775	114	7	backbone	backbone	NOUN
ajst-20775	114	8	network	network	NOUN
ajst-20775	114	9	for	for	ADP
ajst-20775	114	10	landmark	landmark	NOUN
ajst-20775	114	11	detection	detection	NOUN
ajst-20775	114	12	,	,	PUNCT
ajst-20775	114	13	with	with	ADP
ajst-20775	114	14	a	a	DET
ajst-20775	114	15	model	model	NOUN
ajst-20775	114	16	size	size	NOUN
ajst-20775	114	17	of	of	ADP
ajst-20775	114	18	5.0	5.0	NUM
ajst-20775	114	19	mb	mb	NOUN
ajst-20775	114	20	and	and	CCONJ
ajst-20775	114	21	an	an	DET
ajst-20775	114	22	nme	nme	NOUN
ajst-20775	114	23	of	of	ADP
ajst-20775	114	24	5.52	5.52	NUM
ajst-20775	114	25	%	%	NOUN
ajst-20775	114	26	.	.	PUNCT
ajst-20775	115	1	although	although	SCONJ
ajst-20775	115	2	there	there	PRON
ajst-20775	115	3	was	be	VERB
ajst-20775	115	4	improvement	improvement	NOUN
ajst-20775	115	5	in	in	ADP
ajst-20775	115	6	inference	inference	NOUN
ajst-20775	115	7	speed	speed	NOUN
ajst-20775	115	8	and	and	CCONJ
ajst-20775	115	9	model	model	NOUN
ajst-20775	115	10	size	size	NOUN
ajst-20775	115	11	,	,	PUNCT
ajst-20775	115	12	there	there	PRON
ajst-20775	115	13	is	be	VERB
ajst-20775	115	14	still	still	ADV
ajst-20775	115	15	room	room	NOUN
ajst-20775	115	16	for	for	ADP
ajst-20775	115	17	further	further	ADJ
ajst-20775	115	18	optimization	optimization	NOUN
ajst-20775	115	19	.	.	PUNCT
ajst-20775	116	1	the	the	DET
ajst-20775	116	2	method	method	NOUN
ajst-20775	116	3	presented	present	VERB
ajst-20775	116	4	in	in	ADP
ajst-20775	116	5	this	this	DET
ajst-20775	116	6	paper	paper	NOUN
ajst-20775	116	7	surpasses	surpass	VERB
ajst-20775	116	8	the	the	DET
ajst-20775	116	9	above	above	ADJ
ajst-20775	116	10	two	two	NUM
ajst-20775	116	11	methods	method	NOUN
ajst-20775	116	12	in	in	ADP
ajst-20775	116	13	terms	term	NOUN
ajst-20775	116	14	of	of	ADP
ajst-20775	116	15	model	model	NOUN
ajst-20775	116	16	size	size	NOUN
ajst-20775	116	17	and	and	CCONJ
ajst-20775	116	18	inference	inference	NOUN
ajst-20775	116	19	time	time	NOUN
ajst-20775	116	20	,	,	PUNCT
ajst-20775	116	21	requiring	require	VERB
ajst-20775	116	22	only	only	ADV
ajst-20775	116	23	2.71	2.71	NUM
ajst-20775	116	24	mb	mb	NOUN
ajst-20775	116	25	for	for	ADP
ajst-20775	116	26	the	the	DET
ajst-20775	116	27	model	model	NOUN
ajst-20775	116	28	and	and	CCONJ
ajst-20775	116	29	achieving	achieve	VERB
ajst-20775	116	30	an	an	DET
ajst-20775	116	31	inference	inference	NOUN
ajst-20775	116	32	speed	speed	NOUN
ajst-20775	116	33	of	of	ADP
ajst-20775	116	34	just	just	ADV
ajst-20775	116	35	2.5ms	2.5ms	NUM
ajst-20775	116	36	,	,	PUNCT
ajst-20775	116	37	while	while	SCONJ
ajst-20775	116	38	maintaining	maintain	VERB
ajst-20775	116	39	an	an	DET
ajst-20775	116	40	nme	nme	NOUN
ajst-20775	116	41	performance	performance	NOUN
ajst-20775	116	42	of	of	ADP
ajst-20775	116	43	5.21	5.21	NUM
ajst-20775	116	44	%	%	NOUN
ajst-20775	116	45	.	.	PUNCT
ajst-20775	117	1	the	the	DET
ajst-20775	117	2	performance	performance	NOUN
ajst-20775	117	3	improvement	improvement	NOUN
ajst-20775	117	4	of	of	ADP
ajst-20775	117	5	this	this	DET
ajst-20775	117	6	paper	paper	NOUN
ajst-20775	117	7	's	's	PART
ajst-20775	117	8	algorithm	algorithm	NOUN
ajst-20775	117	9	is	be	AUX
ajst-20775	117	10	attributed	attribute	VERB
ajst-20775	117	11	to	to	ADP
ajst-20775	117	12	the	the	DET
ajst-20775	117	13	replacement	replacement	NOUN
ajst-20775	117	14	of	of	ADP
ajst-20775	117	15	traditional	traditional	ADJ
ajst-20775	117	16	convolutional	convolutional	ADJ
ajst-20775	117	17	networks	network	NOUN
ajst-20775	117	18	with	with	ADP
ajst-20775	117	19	pointwise	pointwise	NOUN
ajst-20775	117	20	and	and	CCONJ
ajst-20775	117	21	depthwise	depthwise	NOUN
ajst-20775	117	22	convolutions	convolution	NOUN
ajst-20775	117	23	and	and	CCONJ
ajst-20775	117	24	the	the	DET
ajst-20775	117	25	introduction	introduction	NOUN
ajst-20775	117	26	of	of	ADP
ajst-20775	117	27	ghost	ghost	NOUN
ajst-20775	117	28	channel	channel	NOUN
ajst-20775	117	29	structures	structure	NOUN
ajst-20775	117	30	in	in	ADP
ajst-20775	117	31	the	the	DET
ajst-20775	117	32	gm	gm	PROPN
ajst-20775	117	33	module	module	NOUN
ajst-20775	117	34	,	,	PUNCT
ajst-20775	117	35	effectively	effectively	ADV
ajst-20775	117	36	reducing	reduce	VERB
ajst-20775	117	37	computational	computational	ADJ
ajst-20775	117	38	load	load	NOUN
ajst-20775	117	39	.	.	PUNCT
ajst-20775	118	1	furthermore	furthermore	ADV
ajst-20775	118	2	,	,	PUNCT
ajst-20775	118	3	by	by	ADP
ajst-20775	118	4	employing	employ	VERB
ajst-20775	118	5	a	a	DET
ajst-20775	118	6	re	re	ADJ
ajst-20775	118	7	-	-	NOUN
ajst-20775	118	8	parameterization	parameterization	ADJ
ajst-20775	118	9	structure	structure	NOUN
ajst-20775	118	10	during	during	ADP
ajst-20775	118	11	the	the	DET
ajst-20775	118	12	training	training	NOUN
ajst-20775	118	13	phase	phase	NOUN
ajst-20775	118	14	to	to	PART
ajst-20775	118	15	enhance	enhance	VERB
ajst-20775	118	16	the	the	DET
ajst-20775	118	17	model	model	NOUN
ajst-20775	118	18	's	's	PART
ajst-20775	118	19	expressive	expressive	ADJ
ajst-20775	118	20	capability	capability	NOUN
ajst-20775	118	21	and	and	CCONJ
ajst-20775	118	22	reparameterizing	reparameterize	VERB
ajst-20775	118	23	it	it	PRON
ajst-20775	118	24	into	into	ADP
ajst-20775	118	25	a	a	DET
ajst-20775	118	26	linear	linear	ADJ
ajst-20775	118	27	structure	structure	NOUN
ajst-20775	118	28	for	for	ADP
ajst-20775	118	29	the	the	DET
ajst-20775	118	30	inference	inference	NOUN
ajst-20775	118	31	phase	phase	NOUN
ajst-20775	118	32	,	,	PUNCT
ajst-20775	118	33	this	this	DET
ajst-20775	118	34	method	method	NOUN
ajst-20775	118	35	is	be	AUX
ajst-20775	118	36	able	able	ADJ
ajst-20775	118	37	to	to	PART
ajst-20775	118	38	accelerate	accelerate	VERB
ajst-20775	118	39	the	the	DET
ajst-20775	118	40	model	model	NOUN
ajst-20775	118	41	's	's	PART
ajst-20775	118	42	inference	inference	NOUN
ajst-20775	118	43	speed	speed	NOUN
ajst-20775	118	44	while	while	SCONJ
ajst-20775	118	45	maintaining	maintain	VERB
ajst-20775	118	46	high	high	ADJ
ajst-20775	118	47	accuracy	accuracy	NOUN
ajst-20775	118	48	.	.	PUNCT
ajst-20775	119	1	fatigue	fatigue	NOUN
ajst-20775	119	2	discrimination	discrimination	NOUN
ajst-20775	119	3	model	model	NOUN
ajst-20775	119	4	:	:	PUNCT
ajst-20775	119	5	the	the	DET
ajst-20775	119	6	performance	performance	NOUN
ajst-20775	119	7	of	of	ADP
ajst-20775	119	8	our	our	PRON
ajst-20775	119	9	fatigue	fatigue	NOUN
ajst-20775	119	10	driving	drive	VERB
ajst-20775	119	11	detection	detection	NOUN
ajst-20775	119	12	algorithm	algorithm	NOUN
ajst-20775	119	13	compared	compare	VERB
ajst-20775	119	14	to	to	ADP
ajst-20775	119	15	existing	exist	VERB
ajst-20775	119	16	fatigue	fatigue	NOUN
ajst-20775	119	17	detection	detection	NOUN
ajst-20775	119	18	methods	method	NOUN
ajst-20775	119	19	is	be	AUX
ajst-20775	119	20	shown	show	VERB
ajst-20775	119	21	in	in	ADP
ajst-20775	119	22	table	table	NOUN
ajst-20775	119	23	3	3	NUM
ajst-20775	119	24	.	.	PUNCT
ajst-20775	119	25	table	table	NOUN
ajst-20775	119	26	3	3	NUM
ajst-20775	119	27	.	.	PUNCT
ajst-20775	119	28	landmark	landmark	PROPN
ajst-20775	119	29	detection	detection	PROPN
ajst-20775	119	30	result	result	VERB
ajst-20775	119	31	diagram	diagram	NOUN
ajst-20775	119	32	methods	method	NOUN
ajst-20775	119	33	feature	feature	VERB
ajst-20775	119	34	extraction	extraction	NOUN
ajst-20775	119	35	temporal	temporal	ADJ
ajst-20775	119	36	feature	feature	NOUN
ajst-20775	119	37	accuracy	accuracy	NOUN
ajst-20775	119	38	(	(	PUNCT
ajst-20775	119	39	%	%	INTJ
ajst-20775	119	40	)	)	PUNCT
ajst-20775	119	41	literature[14	literature[14	PROPN
ajst-20775	119	42	]	]	PUNCT
ajst-20775	120	1	hog	hog	PROPN
ajst-20775	120	2	mlp	mlp	PROPN
ajst-20775	120	3	74.9	74.9	NUM
ajst-20775	120	4	literature[15	literature[15	NOUN
ajst-20775	120	5	]	]	X
ajst-20775	121	1	cnn	cnn	PROPN
ajst-20775	121	2	87.5	87.5	NUM
ajst-20775	121	3	literature[16	literature[16	NOUN
ajst-20775	121	4	]	]	PUNCT
ajst-20775	121	5	3d	3d	NUM
ajst-20775	121	6	cgan	cgan	ADJ
ajst-20775	121	7	bilstm	bilstm	NOUN
ajst-20775	121	8	91.2	91.2	NUM
ajst-20775	121	9	ours	ours	PRON
ajst-20775	121	10	retinaface+msgm	retinaface+msgm	PROPN
ajst-20775	121	11	-	-	PUNCT
ajst-20775	121	12	net	net	NOUN
ajst-20775	121	13	transformer	transformer	NOUN
ajst-20775	121	14	91.4	91.4	NUM
ajst-20775	121	15	the	the	DET
ajst-20775	121	16	comparison	comparison	NOUN
ajst-20775	121	17	and	and	CCONJ
ajst-20775	121	18	analysis	analysis	NOUN
ajst-20775	121	19	focus	focus	VERB
ajst-20775	121	20	on	on	ADP
ajst-20775	121	21	both	both	CCONJ
ajst-20775	121	22	facial	facial	ADJ
ajst-20775	121	23	spatial	spatial	ADJ
ajst-20775	121	24	features	feature	NOUN
ajst-20775	121	25	and	and	CCONJ
ajst-20775	121	26	fatigue	fatigue	NOUN
ajst-20775	121	27	temporal	temporal	ADJ
ajst-20775	121	28	characteristics	characteristic	NOUN
ajst-20775	121	29	.	.	PUNCT
ajst-20775	122	1	research	research	NOUN
ajst-20775	123	1	[	[	X
ajst-20775	123	2	14	14	NUM
ajst-20775	123	3	]	]	X
ajst-20775	123	4	utilized	utilize	VERB
ajst-20775	123	5	histogram	histogram	NOUN
ajst-20775	123	6	of	of	ADP
ajst-20775	123	7	oriented	orient	VERB
ajst-20775	123	8	gradients	gradient	NOUN
ajst-20775	123	9	(	(	PUNCT
ajst-20775	123	10	hog	hog	NOUN
ajst-20775	123	11	)	)	PUNCT
ajst-20775	123	12	technology	technology	NOUN
ajst-20775	123	13	to	to	PART
ajst-20775	123	14	locate	locate	VERB
ajst-20775	123	15	faces	face	NOUN
ajst-20775	123	16	and	and	CCONJ
ajst-20775	123	17	landmarks	landmark	NOUN
ajst-20775	123	18	,	,	PUNCT
ajst-20775	123	19	extracting	extract	VERB
ajst-20775	123	20	spatial	spatial	ADJ
ajst-20775	123	21	features	feature	NOUN
ajst-20775	123	22	such	such	ADJ
ajst-20775	123	23	as	as	ADP
ajst-20775	123	24	the	the	DET
ajst-20775	123	25	eye	eye	NOUN
ajst-20775	123	26	aspect	aspect	NOUN
ajst-20775	123	27	ratio	ratio	NOUN
ajst-20775	123	28	(	(	PUNCT
ajst-20775	123	29	ear	ear	NOUN
ajst-20775	123	30	)	)	PUNCT
ajst-20775	123	31	,	,	PUNCT
ajst-20775	123	32	mouth	mouth	NOUN
ajst-20775	123	33	aspect	aspect	PROPN
ajst-20775	123	34	ratio	ratio	NOUN
ajst-20775	123	35	(	(	PUNCT
ajst-20775	123	36	mar	mar	PROPN
ajst-20775	123	37	)	)	PUNCT
ajst-20775	123	38	,	,	PUNCT
ajst-20775	123	39	and	and	CCONJ
ajst-20775	123	40	head	head	NOUN
ajst-20775	123	41	posture	posture	NOUN
ajst-20775	123	42	angles	angle	NOUN
ajst-20775	123	43	.	.	PUNCT
ajst-20775	124	1	it	it	PRON
ajst-20775	124	2	employed	employ	VERB
ajst-20775	124	3	multi	multi	ADJ
ajst-20775	124	4	-	-	ADJ
ajst-20775	124	5	layer	layer	ADJ
ajst-20775	124	6	perceptron	perceptron	NOUN
ajst-20775	124	7	(	(	PUNCT
ajst-20775	124	8	mlp	mlp	PROPN
ajst-20775	124	9	)	)	PUNCT
ajst-20775	124	10	and	and	CCONJ
ajst-20775	124	11	k	k	ADV
ajst-20775	124	12	-	-	PUNCT
ajst-20775	124	13	nearest	near	ADJ
ajst-20775	124	14	neighbors	neighbor	NOUN
ajst-20775	124	15	(	(	PUNCT
ajst-20775	124	16	knn	knn	PROPN
ajst-20775	124	17	)	)	PUNCT
ajst-20775	124	18	algorithms	algorithm	NOUN
ajst-20775	124	19	for	for	ADP
ajst-20775	124	20	fatigue	fatigue	NOUN
ajst-20775	124	21	state	state	NOUN
ajst-20775	124	22	classification	classification	NOUN
ajst-20775	124	23	,	,	PUNCT
ajst-20775	124	24	achieving	achieve	VERB
ajst-20775	124	25	an	an	DET
ajst-20775	124	26	accuracy	accuracy	NOUN
ajst-20775	124	27	of	of	ADP
ajst-20775	124	28	74.9	74.9	NUM
ajst-20775	124	29	%	%	NOUN
ajst-20775	124	30	.	.	PUNCT
ajst-20775	125	1	research[15	research[15	NOUN
ajst-20775	125	2	]	]	PUNCT
ajst-20775	125	3	used	use	VERB
ajst-20775	125	4	a	a	DET
ajst-20775	125	5	2d	2d	NUM
ajst-20775	125	6	convolutional	convolutional	ADJ
ajst-20775	125	7	neural	neural	ADJ
ajst-20775	125	8	network	network	NOUN
ajst-20775	125	9	to	to	PART
ajst-20775	125	10	detect	detect	VERB
ajst-20775	125	11	faces	face	NOUN
ajst-20775	125	12	,	,	PUNCT
ajst-20775	125	13	employed	employ	VERB
ajst-20775	125	14	hough	hough	PROPN
ajst-20775	125	15	transform	transform	VERB
ajst-20775	125	16	to	to	PART
ajst-20775	125	17	locate	locate	VERB
ajst-20775	125	18	the	the	DET
ajst-20775	125	19	driver	driver	NOUN
ajst-20775	125	20	's	's	PART
ajst-20775	125	21	eyes	eye	NOUN
ajst-20775	125	22	,	,	PUNCT
ajst-20775	125	23	and	and	CCONJ
ajst-20775	125	24	determined	determine	VERB
ajst-20775	125	25	the	the	DET
ajst-20775	125	26	eye	eye	NOUN
ajst-20775	125	27	's	's	PART
ajst-20775	125	28	open	open	ADJ
ajst-20775	125	29	or	or	CCONJ
ajst-20775	125	30	closed	closed	ADJ
ajst-20775	125	31	state	state	NOUN
ajst-20775	125	32	.	.	PUNCT
ajst-20775	126	1	fatigue	fatigue	NOUN
ajst-20775	126	2	temporality	temporality	NOUN
ajst-20775	126	3	was	be	AUX
ajst-20775	126	4	analyzed	analyze	VERB
ajst-20775	126	5	using	use	VERB
ajst-20775	126	6	the	the	DET
ajst-20775	126	7	perclos	perclo	NOUN
ajst-20775	126	8	threshold	threshold	NOUN
ajst-20775	126	9	method	method	NOUN
ajst-20775	126	10	,	,	PUNCT
ajst-20775	126	11	ultimately	ultimately	ADV
ajst-20775	126	12	achieving	achieve	VERB
ajst-20775	126	13	an	an	DET
ajst-20775	126	14	accuracy	accuracy	NOUN
ajst-20775	126	15	of	of	ADP
ajst-20775	126	16	87.5	87.5	NUM
ajst-20775	126	17	%	%	NOUN
ajst-20775	126	18	.	.	PUNCT
ajst-20775	127	1	compared	compare	VERB
ajst-20775	127	2	to	to	ADP
ajst-20775	127	3	these	these	DET
ajst-20775	127	4	methods	method	NOUN
ajst-20775	127	5	,	,	PUNCT
ajst-20775	127	6	our	our	PRON
ajst-20775	127	7	approach	approach	NOUN
ajst-20775	127	8	combines	combine	VERB
ajst-20775	127	9	retinaface	retinaface	NOUN
ajst-20775	127	10	and	and	CCONJ
ajst-20775	127	11	the	the	DET
ajst-20775	127	12	multi	multi	ADJ
ajst-20775	127	13	-	-	ADJ
ajst-20775	127	14	scale	scale	ADJ
ajst-20775	127	15	fusion	fusion	NOUN
ajst-20775	127	16	facial	facial	ADJ
ajst-20775	127	17	landmark	landmark	NOUN
ajst-20775	127	18	detection	detection	NOUN
ajst-20775	127	19	network	network	NOUN
ajst-20775	127	20	(	(	PUNCT
ajst-20775	127	21	msgm	msgm	NOUN
ajst-20775	127	22	-	-	PUNCT
ajst-20775	127	23	net	net	NOUN
ajst-20775	127	24	)	)	PUNCT
ajst-20775	127	25	for	for	ADP
ajst-20775	127	26	spatial	spatial	ADJ
ajst-20775	127	27	feature	feature	NOUN
ajst-20775	127	28	extraction	extraction	NOUN
ajst-20775	127	29	,	,	PUNCT
ajst-20775	127	30	and	and	CCONJ
ajst-20775	127	31	processes	process	VERB
ajst-20775	127	32	temporal	temporal	ADJ
ajst-20775	127	33	features	feature	NOUN
ajst-20775	127	34	through	through	ADP
ajst-20775	127	35	the	the	DET
ajst-20775	127	36	transformer	transformer	NOUN
ajst-20775	127	37	model	model	NOUN
ajst-20775	127	38	,	,	PUNCT
ajst-20775	127	39	achieving	achieve	VERB
ajst-20775	127	40	a	a	DET
ajst-20775	127	41	classification	classification	NOUN
ajst-20775	127	42	accuracy	accuracy	NOUN
ajst-20775	127	43	of	of	ADP
ajst-20775	127	44	91.4	91.4	NUM
ajst-20775	127	45	%	%	NOUN
ajst-20775	127	46	.	.	PUNCT
ajst-20775	128	1	although	although	SCONJ
ajst-20775	128	2	the	the	DET
ajst-20775	128	3	detection	detection	NOUN
ajst-20775	128	4	accuracy	accuracy	NOUN
ajst-20775	128	5	in	in	ADP
ajst-20775	128	6	research[16	research[16	PROPN
ajst-20775	128	7	]	]	PUNCT
ajst-20775	128	8	is	be	AUX
ajst-20775	128	9	close	close	ADJ
ajst-20775	128	10	to	to	ADP
ajst-20775	128	11	our	our	PRON
ajst-20775	128	12	algorithm	algorithm	NOUN
ajst-20775	128	13	,	,	PUNCT
ajst-20775	128	14	differing	differ	VERB
ajst-20775	128	15	by	by	ADP
ajst-20775	128	16	only	only	ADV
ajst-20775	128	17	0.02	0.02	NUM
ajst-20775	128	18	%	%	NOUN
ajst-20775	128	19	,	,	PUNCT
ajst-20775	128	20	their	their	PRON
ajst-20775	128	21	method	method	NOUN
ajst-20775	128	22	utilized	utilize	VERB
ajst-20775	128	23	a	a	DET
ajst-20775	128	24	3d	3d	NUM
ajst-20775	128	25	convolutional	convolutional	ADJ
ajst-20775	128	26	generative	generative	ADJ
ajst-20775	128	27	network	network	NOUN
ajst-20775	128	28	,	,	PUNCT
ajst-20775	128	29	with	with	ADP
ajst-20775	128	30	inference	inference	NOUN
ajst-20775	128	31	times	time	NOUN
ajst-20775	128	32	reaching	reach	VERB
ajst-20775	128	33	up	up	ADP
ajst-20775	128	34	to	to	PART
ajst-20775	128	35	25	25	NUM
ajst-20775	128	36	seconds	second	NOUN
ajst-20775	128	37	,	,	PUNCT
ajst-20775	128	38	which	which	PRON
ajst-20775	128	39	does	do	AUX
ajst-20775	128	40	not	not	PART
ajst-20775	128	41	meet	meet	VERB
ajst-20775	128	42	the	the	DET
ajst-20775	128	43	real	real	ADJ
ajst-20775	128	44	-	-	PUNCT
ajst-20775	128	45	time	time	NOUN
ajst-20775	128	46	requirement	requirement	NOUN
ajst-20775	128	47	for	for	ADP
ajst-20775	128	48	fatigue	fatigue	NOUN
ajst-20775	128	49	driving	drive	VERB
ajst-20775	128	50	detection	detection	NOUN
ajst-20775	128	51	.	.	PUNCT
ajst-20775	129	1	4	4	X
ajst-20775	129	2	.	.	X
ajst-20775	129	3	conclusion	conclusion	NOUN
ajst-20775	129	4	this	this	DET
ajst-20775	129	5	paper	paper	NOUN
ajst-20775	129	6	proposes	propose	VERB
ajst-20775	129	7	a	a	DET
ajst-20775	129	8	temporal	temporal	ADJ
ajst-20775	129	9	sequence	sequence	NOUN
ajst-20775	129	10	transformer	transformer	NOUN
ajst-20775	129	11	model	model	NOUN
ajst-20775	129	12	for	for	ADP
ajst-20775	129	13	fatigue	fatigue	NOUN
ajst-20775	129	14	driving	drive	VERB
ajst-20775	129	15	detection	detection	NOUN
ajst-20775	129	16	based	base	VERB
ajst-20775	129	17	on	on	ADP
ajst-20775	129	18	the	the	DET
ajst-20775	129	19	localization	localization	NOUN
ajst-20775	129	20	of	of	ADP
ajst-20775	129	21	driver	driver	NOUN
ajst-20775	129	22	facial	facial	ADJ
ajst-20775	129	23	landmarks	landmark	NOUN
ajst-20775	129	24	.	.	PUNCT
ajst-20775	130	1	by	by	ADP
ajst-20775	130	2	integrating	integrate	VERB
ajst-20775	130	3	the	the	DET
ajst-20775	130	4	mobileone	mobileone	NOUN
ajst-20775	130	5	module	module	NOUN
ajst-20775	130	6	with	with	ADP
ajst-20775	130	7	ghost	ghost	NOUN
ajst-20775	130	8	channel	channel	NOUN
ajst-20775	130	9	technology	technology	NOUN
ajst-20775	130	10	,	,	PUNCT
ajst-20775	130	11	we	we	PRON
ajst-20775	130	12	designed	design	VERB
ajst-20775	130	13	a	a	DET
ajst-20775	130	14	lightweight	lightweight	ADJ
ajst-20775	130	15	gm	gm	PROPN
ajst-20775	130	16	feature	feature	NOUN
ajst-20775	130	17	extraction	extraction	NOUN
ajst-20775	130	18	module	module	NOUN
ajst-20775	130	19	for	for	ADP
ajst-20775	130	20	a	a	DET
ajst-20775	130	21	multi	multi	ADJ
ajst-20775	130	22	-	-	ADJ
ajst-20775	130	23	scale	scale	ADJ
ajst-20775	130	24	fusion	fusion	NOUN
ajst-20775	130	25	facial	facial	ADJ
ajst-20775	130	26	landmark	landmark	NOUN
ajst-20775	130	27	detection	detection	NOUN
ajst-20775	130	28	algorithm	algorithm	NOUN
ajst-20775	130	29	.	.	PUNCT
ajst-20775	131	1	fatigue	fatigue	NOUN
ajst-20775	131	2	feature	feature	NOUN
ajst-20775	131	3	parameter	parameter	NOUN
ajst-20775	131	4	sequences	sequence	NOUN
ajst-20775	131	5	for	for	ADP
ajst-20775	131	6	the	the	DET
ajst-20775	131	7	face	face	NOUN
ajst-20775	131	8	,	,	PUNCT
ajst-20775	131	9	mouth	mouth	NOUN
ajst-20775	131	10	,	,	PUNCT
ajst-20775	131	11	and	and	CCONJ
ajst-20775	131	12	head	head	NOUN
ajst-20775	131	13	posture	posture	NOUN
ajst-20775	131	14	angles	angle	NOUN
ajst-20775	131	15	are	be	AUX
ajst-20775	131	16	extracted	extract	VERB
ajst-20775	131	17	based	base	VERB
ajst-20775	131	18	on	on	ADP
ajst-20775	131	19	landmark	landmark	PROPN
ajst-20775	131	20	information	information	NOUN
ajst-20775	131	21	,	,	PUNCT
ajst-20775	131	22	and	and	CCONJ
ajst-20775	131	23	a	a	DET
ajst-20775	131	24	temporal	temporal	ADJ
ajst-20775	131	25	sequence	sequence	NOUN
ajst-20775	131	26	transformer	transformer	NOUN
ajst-20775	131	27	is	be	AUX
ajst-20775	131	28	utilized	utilize	VERB
ajst-20775	131	29	to	to	PART
ajst-20775	131	30	classify	classify	VERB
ajst-20775	131	31	these	these	DET
ajst-20775	131	32	fatigue	fatigue	NOUN
ajst-20775	131	33	feature	feature	NOUN
ajst-20775	131	34	parameter	parameter	NOUN
ajst-20775	131	35	sequences	sequence	NOUN
ajst-20775	131	36	.	.	PUNCT
ajst-20775	132	1	experiments	experiment	NOUN
ajst-20775	132	2	demonstrate	demonstrate	VERB
ajst-20775	132	3	that	that	SCONJ
ajst-20775	132	4	the	the	DET
ajst-20775	132	5	proposed	propose	VERB
ajst-20775	132	6	algorithm	algorithm	NOUN
ajst-20775	132	7	has	have	VERB
ajst-20775	132	8	good	good	ADJ
ajst-20775	132	9	generalization	generalization	NOUN
ajst-20775	132	10	performance	performance	NOUN
ajst-20775	132	11	and	and	CCONJ
ajst-20775	132	12	high	high	ADJ
ajst-20775	132	13	real	real	ADJ
ajst-20775	132	14	-	-	PUNCT
ajst-20775	132	15	time	time	NOUN
ajst-20775	132	16	detection	detection	NOUN
ajst-20775	132	17	capability	capability	NOUN
ajst-20775	132	18	.	.	PUNCT
ajst-20775	133	1	however	however	ADV
ajst-20775	133	2	,	,	PUNCT
ajst-20775	133	3	this	this	DET
ajst-20775	133	4	study	study	NOUN
ajst-20775	133	5	only	only	ADV
ajst-20775	133	6	utilized	utilize	VERB
ajst-20775	133	7	a	a	DET
ajst-20775	133	8	subset	subset	NOUN
ajst-20775	133	9	of	of	ADP
ajst-20775	133	10	facial	facial	ADJ
ajst-20775	133	11	landmarks	landmark	NOUN
ajst-20775	133	12	.	.	PUNCT
ajst-20775	134	1	in	in	ADP
ajst-20775	134	2	future	future	ADJ
ajst-20775	134	3	work	work	NOUN
ajst-20775	134	4	,	,	PUNCT
ajst-20775	134	5	considering	consider	VERB
ajst-20775	134	6	more	more	ADJ
ajst-20775	134	7	landmarks	landmark	NOUN
ajst-20775	134	8	is	be	AUX
ajst-20775	134	9	planned	plan	VERB
ajst-20775	134	10	to	to	PART
ajst-20775	134	11	further	far	ADV
ajst-20775	134	12	improve	improve	VERB
ajst-20775	134	13	accuracy	accuracy	NOUN
ajst-20775	134	14	.	.	PUNCT
ajst-20775	135	1	acknowledgment	acknowledgment	NOUN
ajst-20775	135	2	this	this	DET
ajst-20775	135	3	work	work	NOUN
ajst-20775	135	4	was	be	AUX
ajst-20775	135	5	supported	support	VERB
ajst-20775	135	6	by	by	ADP
ajst-20775	135	7	the	the	DET
ajst-20775	135	8	national	national	ADJ
ajst-20775	135	9	natural	natural	PROPN
ajst-20775	135	10	science	science	PROPN
ajst-20775	135	11	foundation	foundation	PROPN
ajst-20775	135	12	of	of	ADP
ajst-20775	135	13	china	china	PROPN
ajst-20775	135	14	(	(	PUNCT
ajst-20775	135	15	62072158	62072158	NUM
ajst-20775	135	16	,	,	PUNCT
ajst-20775	135	17	u2004163	u2004163	NUM
ajst-20775	135	18	)	)	PUNCT
ajst-20775	135	19	,	,	PUNCT
ajst-20775	135	20	the	the	DET
ajst-20775	135	21	key	key	ADJ
ajst-20775	135	22	research	research	NOUN
ajst-20775	135	23	and	and	CCONJ
ajst-20775	135	24	development	development	NOUN
ajst-20775	135	25	special	special	ADJ
ajst-20775	135	26	projects	project	NOUN
ajst-20775	135	27	of	of	ADP
ajst-20775	135	28	henan	henan	PROPN
ajst-20775	135	29	province(231111221500	province(231111221500	PROPN
ajst-20775	135	30	)	)	PUNCT
ajst-20775	135	31	,	,	PUNCT
ajst-20775	135	32	and	and	CCONJ
ajst-20775	135	33	science	science	NOUN
ajst-20775	135	34	and	and	CCONJ
ajst-20775	135	35	technology	technology	NOUN
ajst-20775	135	36	project	project	NOUN
ajst-20775	135	37	of	of	ADP
ajst-20775	135	38	henan	henan	PROPN
ajst-20775	135	39	province(232102210158	province(232102210158	PROPN
ajst-20775	135	40	,	,	PUNCT
ajst-20775	135	41	242102210197	242102210197	NUM
ajst-20775	135	42	)	)	PUNCT
ajst-20775	135	43	.	.	PUNCT
ajst-20775	136	1	references	reference	NOUN
ajst-20775	136	2	[	[	X
ajst-20775	136	3	1	1	NUM
ajst-20775	136	4	]	]	SYM
ajst-20775	136	5	facts	fact	NOUN
ajst-20775	136	6	and	and	CCONJ
ajst-20775	136	7	stats	stat	NOUN
ajst-20775	136	8	,	,	PUNCT
ajst-20775	136	9	may	may	AUX
ajst-20775	136	10	2018	2018	NUM
ajst-20775	136	11	.	.	PUNCT
ajst-20775	137	1	[	[	X
ajst-20775	137	2	online	online	X
ajst-20775	137	3	]	]	X
ajst-20775	137	4	.	.	PUNCT
ajst-20775	138	1	available	available	ADJ
ajst-20775	138	2	:	:	PUNCT
ajst-20775	138	3	http://drowsydriving.org/about/facts-and-stats/.	http://drowsydriving.org/about/facts-and-stats/.	PROPN
ajst-20775	138	4	41	41	NUM
ajst-20775	139	1	[	[	X
ajst-20775	139	2	2	2	NUM
ajst-20775	139	3	]	]	SYM
ajst-20775	139	4	su	su	NOUN
ajst-20775	139	5	-	-	PROPN
ajst-20775	139	6	xian	xian	PROPN
ajst-20775	139	7	c	c	PROPN
ajst-20775	139	8	a	a	DET
ajst-20775	139	9	i	i	PROPN
ajst-20775	139	10	,	,	PUNCT
ajst-20775	139	11	chao	chao	PROPN
ajst-20775	139	12	-	-	PUNCT
ajst-20775	139	13	kan	kan	PROPN
ajst-20775	139	14	d	d	PROPN
ajst-20775	139	15	u	u	PROPN
ajst-20775	139	16	,	,	PUNCT
ajst-20775	139	17	si	si	PROPN
ajst-20775	139	18	-	-	PUNCT
ajst-20775	139	19	yi	yi	PROPN
ajst-20775	139	20	z	z	PROPN
ajst-20775	139	21	,	,	PUNCT
ajst-20775	139	22	et	et	PROPN
ajst-20775	139	23	al	al	PROPN
ajst-20775	139	24	.	.	PUNCT
ajst-20775	139	25	fatigue	fatigue	NOUN
ajst-20775	139	26	driving	drive	VERB
ajst-20775	139	27	state	state	NOUN
ajst-20775	139	28	detection	detection	NOUN
ajst-20775	139	29	based	base	VERB
ajst-20775	139	30	on	on	ADP
ajst-20775	139	31	vehicle	vehicle	NOUN
ajst-20775	139	32	running	run	VERB
ajst-20775	139	33	data[j	data[j	NOUN
ajst-20775	139	34	]	]	PUNCT
ajst-20775	139	35	.	.	PUNCT
ajst-20775	139	36	journal	journal	PROPN
ajst-20775	139	37	of	of	ADP
ajst-20775	139	38	transportation	transportation	NOUN
ajst-20775	139	39	systems	system	NOUN
ajst-20775	139	40	engineering	engineering	NOUN
ajst-20775	139	41	and	and	CCONJ
ajst-20775	139	42	information	information	NOUN
ajst-20775	139	43	technology	technology	NOUN
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ajst-20775	139	45	2020	2020	NUM
ajst-20775	139	46	,	,	PUNCT
ajst-20775	139	47	20(4	20(4	NOUN
ajst-20775	139	48	):	):	PUNCT
ajst-20775	139	49	77	77	NUM
ajst-20775	139	50	.	.	PUNCT
ajst-20775	140	1	[	[	X
ajst-20775	140	2	3	3	X
ajst-20775	140	3	]	]	X
ajst-20775	140	4	shujuan	shujuan	PROPN
ajst-20775	140	5	gong	gong	PROPN
ajst-20775	140	6	,	,	PUNCT
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ajst-20775	140	8	zhao	zhao	PROPN
ajst-20775	140	9	,	,	PUNCT
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ajst-20775	140	13	research	research	PROPN
ajst-20775	140	14	on	on	ADP
ajst-20775	140	15	fatigue	fatigue	NOUN
ajst-20775	140	16	driving	drive	VERB
ajst-20775	140	17	detection	detection	NOUN
ajst-20775	140	18	based	base	VERB
ajst-20775	140	19	on	on	ADP
ajst-20775	140	20	multi	multi	ADJ
ajst-20775	140	21	-	-	ADJ
ajst-20775	140	22	physiological	physiological	ADJ
ajst-20775	140	23	signal	signal	NOUN
ajst-20775	140	24	fusion	fusion	NOUN
ajst-20775	140	25	.	.	PUNCT
ajst-20775	141	1	journal	journal	NOUN
ajst-20775	141	2	of	of	ADP
ajst-20775	141	3	transportation	transportation	NOUN
ajst-20775	141	4	systems	system	NOUN
ajst-20775	141	5	engineering	engineering	NOUN
ajst-20775	141	6	and	and	CCONJ
ajst-20775	141	7	information	information	NOUN
ajst-20775	141	8	technology	technology	NOUN
ajst-20775	141	9	.	.	PUNCT
ajst-20775	142	1	2023	2023	NUM
ajst-20775	142	2	,	,	PUNCT
ajst-20775	142	3	vol.143	vol.143	NOUN
ajst-20775	142	4	,	,	PUNCT
ajst-20775	142	5	p.	p.	NOUN
ajst-20775	142	6	4002	4002	NUM
ajst-20775	142	7	.	.	PUNCT
ajst-20775	143	1	[	[	X
ajst-20775	143	2	4	4	X
ajst-20775	143	3	]	]	X
ajst-20775	143	4	yi	yi	PROPN
ajst-20775	143	5	y	y	PROPN
ajst-20775	143	6	,	,	PUNCT
ajst-20775	143	7	zhang	zhang	PROPN
ajst-20775	143	8	h	h	PROPN
ajst-20775	143	9	,	,	PUNCT
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ajst-20775	143	11	w	w	PROPN
ajst-20775	143	12	,	,	PUNCT
ajst-20775	143	13	et	et	PROPN
ajst-20775	143	14	al	al	PROPN
ajst-20775	143	15	.	.	PROPN
ajst-20775	143	16	fatigue	fatigue	NOUN
ajst-20775	143	17	working	work	VERB
ajst-20775	143	18	detection	detection	NOUN
ajst-20775	143	19	based	base	VERB
ajst-20775	143	20	on	on	ADP
ajst-20775	143	21	facial	facial	ADJ
ajst-20775	143	22	multifeature	multifeature	NOUN
ajst-20775	143	23	fusion[j	fusion[j	PROPN
ajst-20775	143	24	]	]	PUNCT
ajst-20775	143	25	.	.	PUNCT
ajst-20775	144	1	ieee	ieee	PROPN
ajst-20775	144	2	sensors	sensor	NOUN
ajst-20775	144	3	journal	journal	PROPN
ajst-20775	144	4	,	,	PUNCT
ajst-20775	144	5	2023	2023	NUM
ajst-20775	144	6	,	,	PUNCT
ajst-20775	144	7	23(6	23(6	NUM
ajst-20775	144	8	):	):	PUNCT
ajst-20775	144	9	5956	5956	NUM
ajst-20775	144	10	-	-	SYM
ajst-20775	144	11	5961	5961	NUM
ajst-20775	144	12	.	.	PUNCT
ajst-20775	145	1	[	[	X
ajst-20775	145	2	5	5	NUM
ajst-20775	145	3	]	]	PUNCT
ajst-20775	145	4	ghourabi	ghourabi	NOUN
ajst-20775	145	5	a	a	PRON
ajst-20775	145	6	,	,	PUNCT
ajst-20775	145	7	ghazouani	ghazouani	PROPN
ajst-20775	145	8	h	h	NOUN
ajst-20775	145	9	,	,	PUNCT
ajst-20775	145	10	barhoumi	barhoumi	PROPN
ajst-20775	145	11	w.	w.	PROPN
ajst-20775	145	12	driver	driver	PROPN
ajst-20775	145	13	drowsiness	drowsiness	PROPN
ajst-20775	145	14	detection	detection	NOUN
ajst-20775	145	15	based	base	VERB
ajst-20775	145	16	on	on	ADP
ajst-20775	145	17	joint	joint	ADJ
ajst-20775	145	18	monitoring	monitoring	NOUN
ajst-20775	145	19	of	of	ADP
ajst-20775	145	20	yawning	yawn	VERB
ajst-20775	145	21	,	,	PUNCT
ajst-20775	145	22	blinking	blink	VERB
ajst-20775	145	23	and	and	CCONJ
ajst-20775	145	24	nodding[c	nodding[c	NOUN
ajst-20775	145	25	]	]	PUNCT
ajst-20775	145	26	.	.	PUNCT
ajst-20775	146	1	2020	2020	NUM
ajst-20775	146	2	ieee	ieee	NOUN
ajst-20775	146	3	16th	16th	ADJ
ajst-20775	146	4	international	international	ADJ
ajst-20775	146	5	conference	conference	NOUN
ajst-20775	146	6	on	on	ADP
ajst-20775	146	7	intelligent	intelligent	ADJ
ajst-20775	146	8	computer	computer	NOUN
ajst-20775	146	9	communication	communication	NOUN
ajst-20775	146	10	and	and	CCONJ
ajst-20775	146	11	processing	processing	NOUN
ajst-20775	146	12	.	.	PUNCT
ajst-20775	147	1	ieee	ieee	PROPN
ajst-20775	147	2	,	,	PUNCT
ajst-20775	147	3	2020	2020	NUM
ajst-20775	147	4	:	:	PUNCT
ajst-20775	147	5	407	407	NUM
ajst-20775	147	6	-	-	SYM
ajst-20775	147	7	414	414	NUM
ajst-20775	147	8	.	.	PUNCT
ajst-20775	148	1	[	[	X
ajst-20775	148	2	6	6	NUM
ajst-20775	148	3	]	]	X
ajst-20775	148	4	dua	dua	PROPN
ajst-20775	148	5	m	m	PROPN
ajst-20775	148	6	,	,	PUNCT
ajst-20775	148	7	shakshi	shakshi	PROPN
ajst-20775	148	8	,	,	PUNCT
ajst-20775	148	9	singla	singla	PROPN
ajst-20775	148	10	r	r	NOUN
ajst-20775	148	11	,	,	PUNCT
ajst-20775	148	12	et	et	PROPN
ajst-20775	148	13	al	al	PROPN
ajst-20775	148	14	.	.	PUNCT
ajst-20775	149	1	deep	deep	PROPN
ajst-20775	149	2	cnn	cnn	PROPN
ajst-20775	149	3	models	model	NOUN
ajst-20775	149	4	-	-	PUNCT
ajst-20775	149	5	based	base	VERB
ajst-20775	149	6	ensemble	ensemble	ADJ
ajst-20775	149	7	approach	approach	NOUN
ajst-20775	149	8	to	to	ADP
ajst-20775	149	9	driver	driver	NOUN
ajst-20775	149	10	drowsiness	drowsiness	NOUN
ajst-20775	149	11	detection[j	detection[j	PROPN
ajst-20775	149	12	]	]	PUNCT
ajst-20775	149	13	.	.	PUNCT
ajst-20775	150	1	neural	neural	ADJ
ajst-20775	150	2	computing	computing	NOUN
ajst-20775	150	3	and	and	CCONJ
ajst-20775	150	4	applications	application	NOUN
ajst-20775	150	5	,	,	PUNCT
ajst-20775	150	6	2021	2021	NUM
ajst-20775	150	7	,	,	PUNCT
ajst-20775	150	8	33	33	NUM
ajst-20775	150	9	:	:	SYM
ajst-20775	150	10	3155	3155	NUM
ajst-20775	150	11	-	-	SYM
ajst-20775	150	12	3168	3168	NUM
ajst-20775	150	13	.	.	PUNCT
ajst-20775	151	1	[	[	X
ajst-20775	151	2	7	7	X
ajst-20775	151	3	]	]	X
ajst-20775	152	1	liu	liu	PROPN
ajst-20775	152	2	m	m	PROPN
ajst-20775	152	3	z	z	PROPN
ajst-20775	152	4	,	,	PUNCT
ajst-20775	152	5	xu	xu	PROPN
ajst-20775	152	6	x	x	PROPN
ajst-20775	152	7	,	,	PUNCT
ajst-20775	152	8	hu	hu	PROPN
ajst-20775	152	9	j	j	PROPN
ajst-20775	152	10	,	,	PUNCT
ajst-20775	152	11	et	et	PROPN
ajst-20775	152	12	al	al	PROPN
ajst-20775	152	13	.	.	PUNCT
ajst-20775	152	14	real	real	ADJ
ajst-20775	152	15	time	time	NOUN
ajst-20775	152	16	detection	detection	NOUN
ajst-20775	152	17	of	of	ADP
ajst-20775	152	18	driver	driver	NOUN
ajst-20775	152	19	fatigue	fatigue	NOUN
ajst-20775	152	20	based	base	VERB
ajst-20775	152	21	on	on	ADP
ajst-20775	152	22	cnn‐lstm[j	cnn‐lstm[j	PROPN
ajst-20775	152	23	]	]	PUNCT
ajst-20775	152	24	.	.	PUNCT
ajst-20775	153	1	iet	iet	PROPN
ajst-20775	153	2	image	image	PROPN
ajst-20775	153	3	processing	processing	NOUN
ajst-20775	153	4	,	,	PUNCT
ajst-20775	153	5	2022	2022	NUM
ajst-20775	153	6	,	,	PUNCT
ajst-20775	153	7	16(2	16(2	NUM
ajst-20775	153	8	):	):	PUNCT
ajst-20775	153	9	576	576	NUM
ajst-20775	153	10	-	-	SYM
ajst-20775	153	11	595	595	NUM
ajst-20775	153	12	.	.	PUNCT
ajst-20775	154	1	[	[	X
ajst-20775	154	2	8	8	NUM
ajst-20775	154	3	]	]	X
ajst-20775	154	4	deng	deng	PROPN
ajst-20775	154	5	j	j	PROPN
ajst-20775	154	6	,	,	PUNCT
ajst-20775	154	7	guo	guo	PROPN
ajst-20775	154	8	j	j	PROPN
ajst-20775	154	9	,	,	PUNCT
ajst-20775	154	10	zhou	zhou	PROPN
ajst-20775	154	11	y	y	PROPN
ajst-20775	154	12	,	,	PUNCT
ajst-20775	154	13	et	et	PROPN
ajst-20775	154	14	al	al	PROPN
ajst-20775	154	15	.	.	PROPN
ajst-20775	154	16	retinaface	retinaface	PROPN
ajst-20775	154	17	:	:	PUNCT
ajst-20775	154	18	single	single	ADJ
ajst-20775	154	19	-	-	PUNCT
ajst-20775	154	20	stage	stage	NOUN
ajst-20775	154	21	dense	dense	ADJ
ajst-20775	154	22	face	face	NOUN
ajst-20775	154	23	localisation	localisation	NOUN
ajst-20775	154	24	in	in	ADP
ajst-20775	154	25	the	the	DET
ajst-20775	154	26	wild[j	wild[j	NOUN
ajst-20775	154	27	]	]	PUNCT
ajst-20775	154	28	.	.	PUNCT
ajst-20775	155	1	arxiv	arxiv	PROPN
ajst-20775	155	2	preprint	preprint	PROPN
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ajst-20775	155	4	,	,	PUNCT
ajst-20775	155	5	2019	2019	NUM
ajst-20775	155	6	.	.	PUNCT
ajst-20775	156	1	[	[	X
ajst-20775	156	2	9	9	NUM
ajst-20775	156	3	]	]	PUNCT
ajst-20775	156	4	vasu	vasu	PROPN
ajst-20775	156	5	p	p	PROPN
ajst-20775	156	6	k	k	PROPN
ajst-20775	156	7	a	a	PROPN
ajst-20775	156	8	,	,	PUNCT
ajst-20775	156	9	gabriel	gabriel	PROPN
ajst-20775	156	10	j	j	PROPN
ajst-20775	156	11	,	,	PUNCT
ajst-20775	156	12	zhu	zhu	PROPN
ajst-20775	156	13	j	j	PROPN
ajst-20775	156	14	,	,	PUNCT
ajst-20775	156	15	et	et	PROPN
ajst-20775	156	16	al	al	PROPN
ajst-20775	156	17	.	.	PUNCT
ajst-20775	156	18	mobileone	mobileone	PROPN
ajst-20775	156	19	:	:	PUNCT
ajst-20775	156	20	an	an	DET
ajst-20775	156	21	improved	improved	ADJ
ajst-20775	156	22	one	one	NUM
ajst-20775	156	23	millisecond	millisecond	NOUN
ajst-20775	156	24	mobile	mobile	ADJ
ajst-20775	156	25	backbone[c	backbone[c	NOUN
ajst-20775	156	26	]	]	PUNCT
ajst-20775	156	27	.	.	PUNCT
ajst-20775	157	1	proceedings	proceeding	NOUN
ajst-20775	157	2	of	of	ADP
ajst-20775	157	3	the	the	DET
ajst-20775	157	4	ieee	ieee	NOUN
ajst-20775	157	5	/	/	SYM
ajst-20775	157	6	cvf	cvf	NOUN
ajst-20775	157	7	conference	conference	NOUN
ajst-20775	157	8	on	on	ADP
ajst-20775	157	9	computer	computer	NOUN
ajst-20775	157	10	vision	vision	NOUN
ajst-20775	157	11	and	and	CCONJ
ajst-20775	157	12	pattern	pattern	NOUN
ajst-20775	157	13	recognition	recognition	NOUN
ajst-20775	157	14	.	.	PUNCT
ajst-20775	158	1	2023	2023	NUM
ajst-20775	158	2	:	:	PUNCT
ajst-20775	158	3	7907	7907	NUM
ajst-20775	158	4	-	-	SYM
ajst-20775	158	5	7917	7917	NUM
ajst-20775	158	6	.	.	PUNCT
ajst-20775	159	1	[	[	X
ajst-20775	159	2	10	10	NUM
ajst-20775	159	3	]	]	X
ajst-20775	159	4	han	han	PROPN
ajst-20775	159	5	k	k	PROPN
ajst-20775	159	6	,	,	PUNCT
ajst-20775	159	7	wang	wang	PROPN
ajst-20775	159	8	y	y	PROPN
ajst-20775	159	9	,	,	PUNCT
ajst-20775	159	10	tian	tian	PROPN
ajst-20775	159	11	q	q	NOUN
ajst-20775	159	12	,	,	PUNCT
ajst-20775	159	13	et	et	PROPN
ajst-20775	159	14	al	al	PROPN
ajst-20775	159	15	.	.	PROPN
ajst-20775	159	16	ghostnet	ghostnet	NOUN
ajst-20775	159	17	:	:	PUNCT
ajst-20775	159	18	more	more	ADJ
ajst-20775	159	19	features	feature	NOUN
ajst-20775	159	20	from	from	ADP
ajst-20775	159	21	cheap	cheap	ADJ
ajst-20775	159	22	operations[c	operations[c	PROPN
ajst-20775	159	23	]	]	PUNCT
ajst-20775	159	24	.	.	PUNCT
ajst-20775	160	1	proceedings	proceeding	NOUN
ajst-20775	160	2	of	of	ADP
ajst-20775	160	3	the	the	DET
ajst-20775	160	4	ieee	ieee	NOUN
ajst-20775	160	5	/	/	SYM
ajst-20775	160	6	cvf	cvf	NOUN
ajst-20775	160	7	conference	conference	NOUN
ajst-20775	160	8	on	on	ADP
ajst-20775	160	9	computer	computer	NOUN
ajst-20775	160	10	vision	vision	NOUN
ajst-20775	160	11	and	and	CCONJ
ajst-20775	160	12	pattern	pattern	NOUN
ajst-20775	160	13	recognition	recognition	NOUN
ajst-20775	160	14	.	.	PUNCT
ajst-20775	161	1	2020	2020	NUM
ajst-20775	161	2	:	:	PUNCT
ajst-20775	161	3	1580	1580	NUM
ajst-20775	161	4	-	-	SYM
ajst-20775	161	5	1589	1589	NUM
ajst-20775	161	6	.	.	PUNCT
ajst-20775	162	1	[	[	X
ajst-20775	162	2	11	11	NUM
ajst-20775	162	3	]	]	PUNCT
ajst-20775	162	4	vaswani	vaswani	NOUN
ajst-20775	162	5	a	a	PRON
ajst-20775	162	6	,	,	PUNCT
ajst-20775	162	7	shazeer	shazeer	NOUN
ajst-20775	162	8	n	n	SYM
ajst-20775	162	9	,	,	PUNCT
ajst-20775	162	10	parmar	parmar	PROPN
ajst-20775	162	11	n	n	CCONJ
ajst-20775	162	12	,	,	PUNCT
ajst-20775	162	13	et	et	PROPN
ajst-20775	162	14	al	al	PROPN
ajst-20775	162	15	.	.	PUNCT
ajst-20775	162	16	attention	attention	NOUN
ajst-20775	162	17	is	be	AUX
ajst-20775	162	18	all	all	PRON
ajst-20775	162	19	you	you	PRON
ajst-20775	162	20	need[j	need[j	VERB
ajst-20775	162	21	]	]	PUNCT
ajst-20775	162	22	.	.	PUNCT
ajst-20775	163	1	advances	advance	NOUN
ajst-20775	163	2	in	in	ADP
ajst-20775	163	3	neural	neural	ADJ
ajst-20775	163	4	information	information	NOUN
ajst-20775	163	5	processing	processing	NOUN
ajst-20775	163	6	systems	system	NOUN
ajst-20775	163	7	,	,	PUNCT
ajst-20775	163	8	2017	2017	NUM
ajst-20775	163	9	,	,	PUNCT
ajst-20775	163	10	30	30	NUM
ajst-20775	163	11	.	.	PUNCT
ajst-20775	164	1	[	[	X
ajst-20775	164	2	12	12	NUM
ajst-20775	164	3	]	]	X
ajst-20775	164	4	wu	wu	PROPN
ajst-20775	164	5	w	w	PROPN
ajst-20775	164	6	,	,	PUNCT
ajst-20775	164	7	qian	qian	PROPN
ajst-20775	164	8	c	c	PROPN
ajst-20775	164	9	,	,	PUNCT
ajst-20775	164	10	yang	yang	PROPN
ajst-20775	164	11	s	s	PROPN
ajst-20775	164	12	,	,	PUNCT
ajst-20775	164	13	et	et	PROPN
ajst-20775	164	14	al	al	PROPN
ajst-20775	164	15	.	.	PUNCT
ajst-20775	165	1	look	look	VERB
ajst-20775	165	2	at	at	ADP
ajst-20775	165	3	boundary	boundary	NOUN
ajst-20775	165	4	:	:	PUNCT
ajst-20775	165	5	a	a	DET
ajst-20775	165	6	boundary	boundary	ADJ
ajst-20775	165	7	-	-	PUNCT
ajst-20775	165	8	aware	aware	ADJ
ajst-20775	165	9	face	face	NOUN
ajst-20775	165	10	alignment	alignment	NOUN
ajst-20775	165	11	algorithm[c	algorithm[c	NOUN
ajst-20775	165	12	]	]	PUNCT
ajst-20775	165	13	.	.	PUNCT
ajst-20775	166	1	proceedings	proceeding	NOUN
ajst-20775	166	2	of	of	ADP
ajst-20775	166	3	the	the	DET
ajst-20775	166	4	ieee	ieee	NOUN
ajst-20775	166	5	conference	conference	NOUN
ajst-20775	166	6	on	on	ADP
ajst-20775	166	7	computer	computer	NOUN
ajst-20775	166	8	vision	vision	NOUN
ajst-20775	166	9	and	and	CCONJ
ajst-20775	166	10	pattern	pattern	NOUN
ajst-20775	166	11	recognition	recognition	NOUN
ajst-20775	166	12	.	.	PUNCT
ajst-20775	167	1	2018	2018	NUM
ajst-20775	167	2	:	:	PUNCT
ajst-20775	167	3	2129	2129	NUM
ajst-20775	167	4	-	-	SYM
ajst-20775	167	5	2138	2138	NUM
ajst-20775	167	6	.	.	PUNCT
ajst-20775	168	1	[	[	X
ajst-20775	168	2	13	13	NUM
ajst-20775	168	3	]	]	X
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ajst-20775	168	5	x	x	PROPN
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ajst-20775	168	12	,	,	PUNCT
ajst-20775	168	13	et	et	PROPN
ajst-20775	168	14	al	al	PROPN
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ajst-20775	168	16	pfld	pfld	PROPN
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ajst-20775	168	18	a	a	DET
ajst-20775	168	19	practical	practical	ADJ
ajst-20775	168	20	facial	facial	ADJ
ajst-20775	168	21	landmark	landmark	NOUN
ajst-20775	168	22	detector[j	detector[j	NOUN
ajst-20775	168	23	]	]	PUNCT
ajst-20775	168	24	.	.	PUNCT
ajst-20775	169	1	arxiv	arxiv	PROPN
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ajst-20775	169	5	1902.10859	1902.10859	NUM
ajst-20775	169	6	,	,	PUNCT
ajst-20775	169	7	2019	2019	NUM
ajst-20775	169	8	.	.	PUNCT
ajst-20775	170	1	[	[	X
ajst-20775	170	2	14	14	NUM
ajst-20775	170	3	]	]	PUNCT
ajst-20775	170	4	a.	a.	NOUN
ajst-20775	170	5	ghourabi	ghourabi	NOUN
ajst-20775	170	6	,	,	PUNCT
ajst-20775	170	7	h.	h.	PROPN
ajst-20775	170	8	ghazouani	ghazouani	PROPN
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ajst-20775	170	12	barhoumi	barhoumi	PROPN
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ajst-20775	170	15	drowsiness	drowsiness	NOUN
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ajst-20775	170	19	joint	joint	ADJ
ajst-20775	170	20	monitoring	monitoring	NOUN
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ajst-20775	170	22	yawning	yawn	VERB
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ajst-20775	170	24	blinking	blink	VERB
ajst-20775	170	25	and	and	CCONJ
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ajst-20775	171	2	ieee	ieee	PROPN
ajst-20775	171	3	16th	16th	ADJ
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ajst-20775	171	5	conference	conference	NOUN
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ajst-20775	172	4	,	,	PUNCT
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ajst-20775	172	6	.	.	PUNCT
ajst-20775	173	1	407–414	407–414	NUM
ajst-20775	173	2	.	.	PUNCT
ajst-20775	174	1	[	[	X
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ajst-20775	174	11	zhong	zhong	PROPN
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ajst-20775	174	14	j.	j.	PROPN
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ajst-20775	174	24	in	in	ADP
ajst-20775	174	25	fatigue	fatigue	NOUN
ajst-20775	174	26	driving	drive	VERB
ajst-20775	174	27	detection	detection	NOUN
ajst-20775	174	28	.	.	PUNCT
ajst-20775	175	1	proceedings	proceeding	NOUN
ajst-20775	175	2	of	of	ADP
ajst-20775	175	3	the	the	DET
ajst-20775	175	4	2019	2019	NUM
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ajst-20775	175	6	conference	conference	NOUN
ajst-20775	175	7	on	on	ADP
ajst-20775	175	8	artificial	artificial	ADJ
ajst-20775	175	9	intelligence	intelligence	NOUN
ajst-20775	175	10	and	and	CCONJ
ajst-20775	175	11	advanced	advanced	ADJ
ajst-20775	175	12	manufacturing	manufacturing	NOUN
ajst-20775	175	13	,	,	PUNCT
ajst-20775	175	14	2019	2019	NUM
ajst-20775	175	15	,	,	PUNCT
ajst-20775	175	16	pp	pp	ADJ
ajst-20775	175	17	.	.	PUNCT
ajst-20775	176	1	1–5	1–5	X
ajst-20775	176	2	.	.	PUNCT
ajst-20775	177	1	[	[	X
ajst-20775	177	2	16	16	NUM
ajst-20775	177	3	]	]	X
ajst-20775	177	4	y.	y.	PROPN
ajst-20775	177	5	hu	hu	PROPN
ajst-20775	177	6	,	,	PUNCT
ajst-20775	177	7	m.	m.	PROPN
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ajst-20775	177	10	c.	c.	PROPN
ajst-20775	177	11	xie	xie	PROPN
ajst-20775	177	12	,	,	PUNCT
ajst-20775	177	13	and	and	CCONJ
ajst-20775	177	14	x.	x.	NOUN
ajst-20775	177	15	lu	lu	PROPN
ajst-20775	177	16	,	,	PUNCT
ajst-20775	177	17	driver	driver	NOUN
ajst-20775	177	18	drowsiness	drowsiness	NOUN
ajst-20775	177	19	recognition	recognition	NOUN
ajst-20775	177	20	via	via	ADP
ajst-20775	177	21	3d	3d	PROPN
ajst-20775	177	22	conditional	conditional	ADJ
ajst-20775	177	23	gan	gan	NOUN
ajst-20775	177	24	and	and	CCONJ
ajst-20775	177	25	two	two	NUM
ajst-20775	177	26	-	-	PUNCT
ajst-20775	177	27	level	level	NOUN
ajst-20775	177	28	attention	attention	NOUN
ajst-20775	177	29	bi	bi	NOUN
ajst-20775	177	30	-	-	NOUN
ajst-20775	177	31	lstm	lstm	ADJ
ajst-20775	177	32	.	.	PUNCT
ajst-20775	178	1	ieee	ieee	NOUN
ajst-20775	178	2	transactions	transaction	NOUN
ajst-20775	178	3	on	on	ADP
ajst-20775	178	4	circuits	circuit	NOUN
ajst-20775	178	5	and	and	CCONJ
ajst-20775	178	6	systems	system	NOUN
ajst-20775	178	7	for	for	ADP
ajst-20775	178	8	video	video	NOUN
ajst-20775	178	9	technology	technology	NOUN
ajst-20775	178	10	,	,	PUNCT
ajst-20775	178	11	vol	vol	NOUN
ajst-20775	178	12	.	.	PROPN
ajst-20775	178	13	30	30	NUM
ajst-20775	178	14	,	,	PUNCT
ajst-20775	178	15	no	no	INTJ
ajst-20775	178	16	.	.	NOUN
ajst-20775	178	17	12	12	NUM
ajst-20775	178	18	,	,	PUNCT
ajst-20775	178	19	pp.4755–4768	pp.4755–4768	NOUN
ajst-20775	178	20	,	,	PUNCT
ajst-20775	178	21	2019	2019	NUM
ajst-20775	178	22	.	.	PUNCT