id	sid	tid	token	lemma	pos
fcis-25145	1	1	frontiers	frontier	NOUN
fcis-25145	1	2	in	in	ADP
fcis-25145	1	3	computing	computing	NOUN
fcis-25145	1	4	and	and	CCONJ
fcis-25145	1	5	intelligent	intelligent	ADJ
fcis-25145	1	6	systems	system	NOUN
fcis-25145	1	7	issn	issn	VERB
fcis-25145	1	8	:	:	PUNCT
fcis-25145	1	9	2832	2832	NUM
fcis-25145	1	10	-	-	SYM
fcis-25145	1	11	6024	6024	NUM
fcis-25145	1	12	|	|	NOUN
fcis-25145	1	13	vol	vol	NOUN
fcis-25145	1	14	.	.	PROPN
fcis-25145	2	1	9	9	NUM
fcis-25145	2	2	,	,	PUNCT
fcis-25145	2	3	no	no	INTJ
fcis-25145	2	4	.	.	NOUN
fcis-25145	2	5	2	2	NUM
fcis-25145	2	6	,	,	PUNCT
fcis-25145	2	7	2024	2024	NUM
fcis-25145	2	8	75	75	NUM
fcis-25145	2	9	research	research	NOUN
fcis-25145	2	10	and	and	CCONJ
fcis-25145	2	11	analysis	analysis	NOUN
fcis-25145	2	12	of	of	ADP
fcis-25145	2	13	facial	facial	ADJ
fcis-25145	2	14	expression	expression	NOUN
fcis-25145	2	15	recognition	recognition	NOUN
fcis-25145	2	16	based	base	VERB
fcis-25145	2	17	on	on	ADP
fcis-25145	2	18	deep	deep	ADJ
fcis-25145	2	19	neural	neural	ADJ
fcis-25145	2	20	networks	network	NOUN
fcis-25145	2	21	yali	yali	PROPN
fcis-25145	2	22	yu	yu	PROPN
fcis-25145	2	23	guangdong	guangdong	PROPN
fcis-25145	2	24	university	university	PROPN
fcis-25145	2	25	of	of	ADP
fcis-25145	2	26	science	science	NOUN
fcis-25145	2	27	and	and	CCONJ
fcis-25145	2	28	technology	technology	NOUN
fcis-25145	2	29	,	,	PUNCT
fcis-25145	2	30	dongguan	dongguan	PROPN
fcis-25145	2	31	guangdong	guangdong	PROPN
fcis-25145	2	32	,	,	PUNCT
fcis-25145	2	33	523668	523668	NUM
fcis-25145	2	34	,	,	PUNCT
fcis-25145	2	35	china	china	PROPN
fcis-25145	2	36	abstract	abstract	PROPN
fcis-25145	2	37	:	:	PUNCT
fcis-25145	2	38	since	since	SCONJ
fcis-25145	2	39	the	the	DET
fcis-25145	2	40	traditional	traditional	ADJ
fcis-25145	2	41	feature	feature	NOUN
fcis-25145	2	42	extraction	extraction	NOUN
fcis-25145	2	43	algorithm	algorithm	NOUN
fcis-25145	2	44	can	can	AUX
fcis-25145	2	45	not	not	PART
fcis-25145	2	46	extract	extract	VERB
fcis-25145	2	47	a	a	DET
fcis-25145	2	48	large	large	ADJ
fcis-25145	2	49	number	number	NOUN
fcis-25145	2	50	of	of	ADP
fcis-25145	2	51	effective	effective	ADJ
fcis-25145	2	52	high	high	ADJ
fcis-25145	2	53	-	-	PUNCT
fcis-25145	2	54	dimensional	dimensional	ADJ
fcis-25145	2	55	expression	expression	NOUN
fcis-25145	2	56	features	feature	NOUN
fcis-25145	2	57	,	,	PUNCT
fcis-25145	2	58	and	and	CCONJ
fcis-25145	2	59	the	the	DET
fcis-25145	2	60	traditional	traditional	ADJ
fcis-25145	2	61	convolutional	convolutional	ADJ
fcis-25145	2	62	network	network	NOUN
fcis-25145	2	63	model	model	NOUN
fcis-25145	2	64	has	have	VERB
fcis-25145	2	65	a	a	DET
fcis-25145	2	66	large	large	ADJ
fcis-25145	2	67	number	number	NOUN
fcis-25145	2	68	of	of	ADP
fcis-25145	2	69	parameters	parameter	NOUN
fcis-25145	2	70	in	in	ADP
fcis-25145	2	71	expression	expression	NOUN
fcis-25145	2	72	recognition	recognition	NOUN
fcis-25145	2	73	and	and	CCONJ
fcis-25145	2	74	weak	weak	ADJ
fcis-25145	2	75	generalization	generalization	NOUN
fcis-25145	2	76	ability	ability	NOUN
fcis-25145	2	77	,	,	PUNCT
fcis-25145	2	78	this	this	DET
fcis-25145	2	79	paper	paper	NOUN
fcis-25145	2	80	selects	select	VERB
fcis-25145	2	81	the	the	DET
fcis-25145	2	82	deep	deep	ADJ
fcis-25145	2	83	convolutional	convolutional	ADJ
fcis-25145	2	84	neural	neural	ADJ
fcis-25145	2	85	network	network	NOUN
fcis-25145	2	86	xception	xception	NOUN
fcis-25145	2	87	architecture	architecture	NOUN
fcis-25145	2	88	as	as	ADP
fcis-25145	2	89	the	the	DET
fcis-25145	2	90	basis	basis	NOUN
fcis-25145	2	91	for	for	ADP
fcis-25145	2	92	improvement	improvement	NOUN
fcis-25145	2	93	.	.	PUNCT
fcis-25145	3	1	the	the	DET
fcis-25145	3	2	core	core	NOUN
fcis-25145	3	3	operations	operation	NOUN
fcis-25145	3	4	in	in	ADP
fcis-25145	3	5	the	the	DET
fcis-25145	3	6	model	model	NOUN
fcis-25145	3	7	are	be	AUX
fcis-25145	3	8	residual	residual	ADJ
fcis-25145	3	9	module	module	NOUN
fcis-25145	3	10	and	and	CCONJ
fcis-25145	3	11	depthwise	depthwise	NOUN
fcis-25145	3	12	separable	separable	ADJ
fcis-25145	3	13	convolution	convolution	NOUN
fcis-25145	3	14	,	,	PUNCT
fcis-25145	3	15	and	and	CCONJ
fcis-25145	3	16	relu6	relu6	PROPN
fcis-25145	3	17	activation	activation	NOUN
fcis-25145	3	18	function	function	NOUN
fcis-25145	3	19	is	be	AUX
fcis-25145	3	20	used	use	VERB
fcis-25145	3	21	.	.	PUNCT
fcis-25145	4	1	the	the	DET
fcis-25145	4	2	improved	improved	ADJ
fcis-25145	4	3	model	model	NOUN
fcis-25145	4	4	is	be	AUX
fcis-25145	4	5	trained	train	VERB
fcis-25145	4	6	and	and	CCONJ
fcis-25145	4	7	tested	test	VERB
fcis-25145	4	8	using	use	VERB
fcis-25145	4	9	the	the	DET
fcis-25145	4	10	public	public	ADJ
fcis-25145	4	11	dataset	dataset	NOUN
fcis-25145	4	12	ck+	ck+	NOUN
fcis-25145	4	13	.	.	PUNCT
fcis-25145	5	1	through	through	ADP
fcis-25145	5	2	multiple	multiple	ADJ
fcis-25145	5	3	training	training	NOUN
fcis-25145	5	4	experiments	experiment	NOUN
fcis-25145	5	5	,	,	PUNCT
fcis-25145	5	6	the	the	DET
fcis-25145	5	7	results	result	NOUN
fcis-25145	5	8	show	show	VERB
fcis-25145	5	9	that	that	SCONJ
fcis-25145	5	10	the	the	DET
fcis-25145	5	11	improved	improved	ADJ
fcis-25145	5	12	model	model	NOUN
fcis-25145	5	13	has	have	AUX
fcis-25145	5	14	achieved	achieve	VERB
fcis-25145	5	15	a	a	DET
fcis-25145	5	16	certain	certain	ADJ
fcis-25145	5	17	level	level	NOUN
fcis-25145	5	18	of	of	ADP
fcis-25145	5	19	facial	facial	ADJ
fcis-25145	5	20	expression	expression	NOUN
fcis-25145	5	21	recognition	recognition	NOUN
fcis-25145	5	22	performance	performance	NOUN
fcis-25145	5	23	.	.	PUNCT
fcis-25145	6	1	keywords	keyword	NOUN
fcis-25145	6	2	:	:	PUNCT
fcis-25145	6	3	expression	expression	NOUN
fcis-25145	6	4	recognition	recognition	NOUN
fcis-25145	6	5	;	;	PUNCT
fcis-25145	6	6	neural	neural	ADJ
fcis-25145	6	7	network	network	NOUN
fcis-25145	6	8	;	;	PUNCT
fcis-25145	6	9	depthwise	depthwise	VERB
fcis-25145	6	10	separable	separable	ADJ
fcis-25145	6	11	convolution	convolution	NOUN
fcis-25145	6	12	.	.	PUNCT
fcis-25145	7	1	1	1	X
fcis-25145	7	2	.	.	X
fcis-25145	7	3	introduction	introduction	NOUN
fcis-25145	7	4	with	with	ADP
fcis-25145	7	5	the	the	DET
fcis-25145	7	6	development	development	NOUN
fcis-25145	7	7	of	of	ADP
fcis-25145	7	8	internet	internet	NOUN
fcis-25145	7	9	technology	technology	NOUN
fcis-25145	7	10	and	and	CCONJ
fcis-25145	7	11	intelligent	intelligent	ADJ
fcis-25145	7	12	machines	machine	NOUN
fcis-25145	7	13	,	,	PUNCT
fcis-25145	7	14	it	it	PRON
fcis-25145	7	15	is	be	AUX
fcis-25145	7	16	more	more	ADV
fcis-25145	7	17	and	and	CCONJ
fcis-25145	7	18	more	more	ADV
fcis-25145	7	19	important	important	ADJ
fcis-25145	7	20	for	for	SCONJ
fcis-25145	7	21	machine	machine	NOUN
fcis-25145	7	22	intelligence	intelligence	NOUN
fcis-25145	7	23	to	to	PART
fcis-25145	7	24	recognize	recognize	VERB
fcis-25145	7	25	inner	inner	ADJ
fcis-25145	7	26	feelings	feeling	NOUN
fcis-25145	7	27	and	and	CCONJ
fcis-25145	7	28	needs	need	VERB
fcis-25145	7	29	through	through	ADP
fcis-25145	7	30	human	human	ADJ
fcis-25145	7	31	expressions	expression	NOUN
fcis-25145	7	32	.	.	PUNCT
fcis-25145	8	1	facial	facial	ADJ
fcis-25145	8	2	expressions	expression	NOUN
fcis-25145	8	3	are	be	AUX
fcis-25145	8	4	a	a	DET
fcis-25145	8	5	nonverbal	nonverbal	ADJ
fcis-25145	8	6	form	form	NOUN
fcis-25145	8	7	of	of	ADP
fcis-25145	8	8	communication	communication	NOUN
fcis-25145	8	9	,	,	PUNCT
fcis-25145	8	10	but	but	CCONJ
fcis-25145	8	11	the	the	DET
fcis-25145	8	12	amount	amount	NOUN
fcis-25145	8	13	of	of	ADP
fcis-25145	8	14	information	information	NOUN
fcis-25145	8	15	conveyed	convey	VERB
fcis-25145	8	16	through	through	ADP
fcis-25145	8	17	facial	facial	ADJ
fcis-25145	8	18	expressions	expression	NOUN
fcis-25145	8	19	is	be	AUX
fcis-25145	8	20	as	as	ADV
fcis-25145	8	21	high	high	ADJ
fcis-25145	8	22	as	as	ADP
fcis-25145	8	23	55	55	NUM
fcis-25145	8	24	%	%	NOUN
fcis-25145	8	25	,	,	PUNCT
fcis-25145	8	26	indicating	indicate	VERB
fcis-25145	8	27	the	the	DET
fcis-25145	8	28	importance	importance	NOUN
fcis-25145	8	29	of	of	ADP
fcis-25145	8	30	facial	facial	ADJ
fcis-25145	8	31	expression	expression	NOUN
fcis-25145	8	32	recognition	recognition	NOUN
fcis-25145	8	33	in	in	ADP
fcis-25145	8	34	the	the	DET
fcis-25145	8	35	process	process	NOUN
fcis-25145	8	36	of	of	ADP
fcis-25145	8	37	communication	communication	NOUN
fcis-25145	8	38	.	.	PUNCT
fcis-25145	9	1	facial	facial	ADJ
fcis-25145	9	2	expression	expression	NOUN
fcis-25145	9	3	recognition	recognition	NOUN
fcis-25145	9	4	is	be	AUX
fcis-25145	9	5	the	the	DET
fcis-25145	9	6	foundation	foundation	NOUN
fcis-25145	9	7	of	of	ADP
fcis-25145	9	8	emotional	emotional	ADJ
fcis-25145	9	9	understanding	understanding	NOUN
fcis-25145	9	10	,	,	PUNCT
fcis-25145	9	11	which	which	PRON
fcis-25145	9	12	refers	refer	VERB
fcis-25145	9	13	to	to	ADP
fcis-25145	9	14	separating	separate	VERB
fcis-25145	9	15	specific	specific	ADJ
fcis-25145	9	16	expression	expression	NOUN
fcis-25145	9	17	states	state	NOUN
fcis-25145	9	18	from	from	ADP
fcis-25145	9	19	facial	facial	ADJ
fcis-25145	9	20	images	image	NOUN
fcis-25145	9	21	or	or	CCONJ
fcis-25145	9	22	video	video	NOUN
fcis-25145	9	23	sequences	sequence	NOUN
fcis-25145	9	24	with	with	ADP
fcis-25145	9	25	facial	facial	ADJ
fcis-25145	9	26	features	feature	NOUN
fcis-25145	9	27	,	,	PUNCT
fcis-25145	9	28	and	and	CCONJ
fcis-25145	9	29	then	then	ADV
fcis-25145	9	30	determining	determine	VERB
fcis-25145	9	31	the	the	DET
fcis-25145	9	32	psychological	psychological	ADJ
fcis-25145	9	33	state	state	NOUN
fcis-25145	9	34	and	and	CCONJ
fcis-25145	9	35	inner	inner	ADJ
fcis-25145	9	36	emotions	emotion	NOUN
fcis-25145	9	37	of	of	ADP
fcis-25145	9	38	the	the	DET
fcis-25145	9	39	recognition	recognition	NOUN
fcis-25145	9	40	object	object	NOUN
fcis-25145	9	41	,	,	PUNCT
fcis-25145	9	42	achieving	achieve	VERB
fcis-25145	9	43	computer	computer	NOUN
fcis-25145	9	44	understanding	understanding	NOUN
fcis-25145	9	45	and	and	CCONJ
fcis-25145	9	46	recognition	recognition	NOUN
fcis-25145	9	47	of	of	ADP
fcis-25145	9	48	facial	facial	ADJ
fcis-25145	9	49	expressions	expression	NOUN
fcis-25145	9	50	.	.	PUNCT
fcis-25145	10	1	facial	facial	ADJ
fcis-25145	10	2	expressions	expression	NOUN
fcis-25145	10	3	express	express	VERB
fcis-25145	10	4	different	different	ADJ
fcis-25145	10	5	emotions	emotion	NOUN
fcis-25145	10	6	through	through	ADP
fcis-25145	10	7	the	the	DET
fcis-25145	10	8	different	different	ADJ
fcis-25145	10	9	activity	activity	NOUN
fcis-25145	10	10	states	state	NOUN
fcis-25145	10	11	of	of	ADP
fcis-25145	10	12	facial	facial	ADJ
fcis-25145	10	13	muscles	muscle	NOUN
fcis-25145	10	14	,	,	PUNCT
fcis-25145	10	15	such	such	ADJ
fcis-25145	10	16	as	as	ADP
fcis-25145	10	17	when	when	SCONJ
fcis-25145	10	18	happy	happy	ADJ
fcis-25145	10	19	,	,	PUNCT
fcis-25145	10	20	the	the	DET
fcis-25145	10	21	corners	corner	NOUN
fcis-25145	10	22	of	of	ADP
fcis-25145	10	23	the	the	DET
fcis-25145	10	24	mouth	mouth	NOUN
fcis-25145	10	25	retract	retract	VERB
fcis-25145	10	26	and	and	CCONJ
fcis-25145	10	27	lift	lift	VERB
fcis-25145	10	28	upwards	upwards	ADV
fcis-25145	10	29	,	,	PUNCT
fcis-25145	10	30	the	the	DET
fcis-25145	10	31	cheeks	cheek	NOUN
fcis-25145	10	32	lift	lift	VERB
fcis-25145	10	33	upwards	upwards	ADV
fcis-25145	10	34	,	,	PUNCT
fcis-25145	10	35	and	and	CCONJ
fcis-25145	10	36	crow	crow	NOUN
fcis-25145	10	37	's	's	PART
fcis-25145	10	38	feet	foot	NOUN
fcis-25145	10	39	increase	increase	NOUN
fcis-25145	10	40	;	;	PUNCT
fcis-25145	10	41	when	when	SCONJ
fcis-25145	10	42	feeling	feel	VERB
fcis-25145	10	43	sad	sad	ADJ
fcis-25145	10	44	,	,	PUNCT
fcis-25145	10	45	the	the	DET
fcis-25145	10	46	corners	corner	NOUN
fcis-25145	10	47	of	of	ADP
fcis-25145	10	48	the	the	DET
fcis-25145	10	49	mouth	mouth	NOUN
fcis-25145	10	50	droop	droop	NOUN
fcis-25145	10	51	,	,	PUNCT
fcis-25145	10	52	the	the	DET
fcis-25145	10	53	lips	lip	NOUN
fcis-25145	10	54	are	be	AUX
fcis-25145	10	55	tightly	tightly	ADV
fcis-25145	10	56	closed	closed	ADJ
fcis-25145	10	57	,	,	PUNCT
fcis-25145	10	58	and	and	CCONJ
fcis-25145	10	59	the	the	DET
fcis-25145	10	60	eyebrows	eyebrow	NOUN
fcis-25145	10	61	tighten	tighten	VERB
fcis-25145	10	62	and	and	CCONJ
fcis-25145	10	63	even	even	ADV
fcis-25145	10	64	wrinkle	wrinkle	NOUN
fcis-25145	10	65	into	into	ADP
fcis-25145	10	66	the	the	DET
fcis-25145	10	67	shape	shape	NOUN
fcis-25145	10	68	of	of	ADP
fcis-25145	10	69	"	"	PUNCT
fcis-25145	10	70	inverted	invert	VERB
fcis-25145	10	71	eight	eight	NUM
fcis-25145	10	72	"	"	PUNCT
fcis-25145	10	73	.	.	PUNCT
fcis-25145	11	1	in	in	ADP
fcis-25145	11	2	1971	1971	NUM
fcis-25145	11	3	,	,	PUNCT
fcis-25145	11	4	psychologists	psychologist	NOUN
fcis-25145	11	5	ekman	ekman	PROPN
fcis-25145	11	6	et	et	PROPN
fcis-25145	11	7	al	al	PROPN
fcis-25145	11	8	.	.	PUNCT
fcis-25145	12	1	[	[	X
fcis-25145	12	2	1	1	X
fcis-25145	12	3	]	]	PUNCT
fcis-25145	12	4	made	make	VERB
fcis-25145	12	5	groundbreaking	groundbreake	VERB
fcis-25145	12	6	work	work	NOUN
fcis-25145	12	7	on	on	ADP
fcis-25145	12	8	facial	facial	ADJ
fcis-25145	12	9	expression	expression	NOUN
fcis-25145	12	10	recognition	recognition	NOUN
fcis-25145	12	11	,	,	PUNCT
fcis-25145	12	12	first	first	ADV
fcis-25145	12	13	proposing	propose	VERB
fcis-25145	12	14	that	that	SCONJ
fcis-25145	12	15	humans	human	NOUN
fcis-25145	12	16	have	have	VERB
fcis-25145	12	17	six	six	NUM
fcis-25145	12	18	basic	basic	ADJ
fcis-25145	12	19	emotions	emotion	NOUN
fcis-25145	12	20	that	that	PRON
fcis-25145	12	21	reflect	reflect	VERB
fcis-25145	12	22	their	their	PRON
fcis-25145	12	23	unique	unique	ADJ
fcis-25145	12	24	psychological	psychological	ADJ
fcis-25145	12	25	activities	activity	NOUN
fcis-25145	12	26	,	,	PUNCT
fcis-25145	12	27	and	and	CCONJ
fcis-25145	12	28	dividing	divide	VERB
fcis-25145	12	29	facial	facial	ADJ
fcis-25145	12	30	expressions	expression	NOUN
fcis-25145	12	31	into	into	ADP
fcis-25145	12	32	six	six	NUM
fcis-25145	12	33	basic	basic	ADJ
fcis-25145	12	34	expressions	expression	NOUN
fcis-25145	12	35	:	:	PUNCT
fcis-25145	12	36	happy	happy	ADJ
fcis-25145	12	37	,	,	PUNCT
fcis-25145	12	38	sad	sad	ADJ
fcis-25145	12	39	,	,	PUNCT
fcis-25145	12	40	surprised	surprised	ADJ
fcis-25145	12	41	,	,	PUNCT
fcis-25145	12	42	fearful	fearful	ADJ
fcis-25145	12	43	,	,	PUNCT
fcis-25145	12	44	angry	angry	ADJ
fcis-25145	12	45	,	,	PUNCT
fcis-25145	12	46	and	and	CCONJ
fcis-25145	12	47	disgusted	disgusted	ADJ
fcis-25145	12	48	.	.	PUNCT
fcis-25145	13	1	in	in	ADP
fcis-25145	13	2	recent	recent	ADJ
fcis-25145	13	3	years	year	NOUN
fcis-25145	13	4	,	,	PUNCT
fcis-25145	13	5	the	the	DET
fcis-25145	13	6	application	application	NOUN
fcis-25145	13	7	fields	field	NOUN
fcis-25145	13	8	of	of	ADP
fcis-25145	13	9	facial	facial	ADJ
fcis-25145	13	10	expression	expression	NOUN
fcis-25145	13	11	recognition	recognition	NOUN
fcis-25145	13	12	have	have	AUX
fcis-25145	13	13	gradually	gradually	ADV
fcis-25145	13	14	expanded	expand	VERB
fcis-25145	13	15	,	,	PUNCT
fcis-25145	13	16	such	such	ADJ
fcis-25145	13	17	as	as	ADP
fcis-25145	13	18	remote	remote	ADJ
fcis-25145	13	19	classroom	classroom	NOUN
fcis-25145	13	20	education	education	NOUN
fcis-25145	13	21	,	,	PUNCT
fcis-25145	13	22	fatigue	fatigue	NOUN
fcis-25145	13	23	driving	driving	NOUN
fcis-25145	13	24	,	,	PUNCT
fcis-25145	13	25	medical	medical	ADJ
fcis-25145	13	26	care	care	NOUN
fcis-25145	13	27	,	,	PUNCT
fcis-25145	13	28	etc	etc	X
fcis-25145	13	29	.	.	X
fcis-25145	13	30	,	,	PUNCT
fcis-25145	13	31	greatly	greatly	ADV
fcis-25145	13	32	promoting	promote	VERB
fcis-25145	13	33	social	social	ADJ
fcis-25145	13	34	progress	progress	NOUN
fcis-25145	13	35	and	and	CCONJ
fcis-25145	13	36	improving	improve	VERB
fcis-25145	13	37	living	living	NOUN
fcis-25145	13	38	standards	standard	NOUN
fcis-25145	13	39	.	.	PUNCT
fcis-25145	14	1	there	there	PRON
fcis-25145	14	2	are	be	VERB
fcis-25145	14	3	two	two	NUM
fcis-25145	14	4	common	common	ADJ
fcis-25145	14	5	methods	method	NOUN
fcis-25145	14	6	for	for	ADP
fcis-25145	14	7	facial	facial	ADJ
fcis-25145	14	8	expression	expression	NOUN
fcis-25145	14	9	recognition	recognition	NOUN
fcis-25145	14	10	.	.	PUNCT
fcis-25145	15	1	the	the	DET
fcis-25145	15	2	first	first	ADJ
fcis-25145	15	3	is	be	AUX
fcis-25145	15	4	traditional	traditional	ADJ
fcis-25145	15	5	feature	feature	NOUN
fcis-25145	15	6	extraction	extraction	NOUN
fcis-25145	15	7	methods	method	NOUN
fcis-25145	15	8	,	,	PUNCT
fcis-25145	15	9	such	such	ADJ
fcis-25145	15	10	as	as	ADP
fcis-25145	15	11	local	local	ADJ
fcis-25145	15	12	binary	binary	ADJ
fcis-25145	15	13	patterns	pattern	NOUN
fcis-25145	15	14	(	(	PUNCT
fcis-25145	15	15	lbp	lbp	PROPN
fcis-25145	15	16	)	)	PUNCT
fcis-25145	15	17	,	,	PUNCT
fcis-25145	15	18	active	active	ADJ
fcis-25145	15	19	appearance	appearance	NOUN
fcis-25145	15	20	models	model	NOUN
fcis-25145	15	21	(	(	PUNCT
fcis-25145	15	22	aam	aam	NOUN
fcis-25145	15	23	)	)	PUNCT
fcis-25145	15	24	,	,	PUNCT
fcis-25145	15	25	and	and	CCONJ
fcis-25145	15	26	other	other	ADJ
fcis-25145	15	27	algorithms	algorithm	NOUN
fcis-25145	15	28	.	.	PUNCT
fcis-25145	16	1	the	the	DET
fcis-25145	16	2	second	second	ADJ
fcis-25145	16	3	method	method	NOUN
fcis-25145	16	4	is	be	AUX
fcis-25145	16	5	to	to	PART
fcis-25145	16	6	extract	extract	VERB
fcis-25145	16	7	facial	facial	ADJ
fcis-25145	16	8	expression	expression	NOUN
fcis-25145	16	9	features	feature	VERB
fcis-25145	16	10	through	through	ADP
fcis-25145	16	11	deep	deep	ADJ
fcis-25145	16	12	neural	neural	ADJ
fcis-25145	16	13	networks	network	NOUN
fcis-25145	16	14	,	,	PUNCT
fcis-25145	16	15	by	by	ADP
fcis-25145	16	16	constructing	construct	VERB
fcis-25145	16	17	a	a	DET
fcis-25145	16	18	neural	neural	ADJ
fcis-25145	16	19	network	network	NOUN
fcis-25145	16	20	model	model	NOUN
fcis-25145	16	21	with	with	ADP
fcis-25145	16	22	fewer	few	ADJ
fcis-25145	16	23	parameters	parameter	NOUN
fcis-25145	16	24	and	and	CCONJ
fcis-25145	16	25	smaller	small	ADJ
fcis-25145	16	26	size	size	NOUN
fcis-25145	16	27	,	,	PUNCT
fcis-25145	16	28	and	and	CCONJ
fcis-25145	16	29	training	training	NOUN
fcis-25145	16	30	and	and	CCONJ
fcis-25145	16	31	predicting	predict	VERB
fcis-25145	16	32	the	the	DET
fcis-25145	16	33	model	model	NOUN
fcis-25145	16	34	for	for	ADP
fcis-25145	16	35	facial	facial	ADJ
fcis-25145	16	36	expression	expression	NOUN
fcis-25145	16	37	recognition	recognition	NOUN
fcis-25145	16	38	.	.	PUNCT
fcis-25145	17	1	dang	dang	INTJ
fcis-25145	17	2	xin	xin	PROPN
fcis-25145	17	3	et	et	PROPN
fcis-25145	17	4	al	al	PROPN
fcis-25145	17	5	.	.	PROPN
fcis-25145	17	6	proposed	propose	VERB
fcis-25145	17	7	a	a	DET
fcis-25145	17	8	network	network	NOUN
fcis-25145	17	9	model	model	NOUN
fcis-25145	17	10	for	for	ADP
fcis-25145	17	11	facial	facial	ADJ
fcis-25145	17	12	expression	expression	NOUN
fcis-25145	17	13	recognition	recognition	NOUN
fcis-25145	17	14	based	base	VERB
fcis-25145	17	15	on	on	ADP
fcis-25145	17	16	the	the	DET
fcis-25145	17	17	driving	drive	VERB
fcis-25145	17	18	status	status	NOUN
fcis-25145	17	19	of	of	ADP
fcis-25145	17	20	drivers	driver	NOUN
fcis-25145	17	21	,	,	PUNCT
fcis-25145	17	22	which	which	PRON
fcis-25145	17	23	introduced	introduce	VERB
fcis-25145	17	24	the	the	DET
fcis-25145	17	25	yolov5	yolov5	NOUN
fcis-25145	17	26	module	module	NOUN
fcis-25145	17	27	to	to	PART
fcis-25145	17	28	enhance	enhance	VERB
fcis-25145	17	29	the	the	DET
fcis-25145	17	30	network	network	NOUN
fcis-25145	17	31	's	's	PART
fcis-25145	17	32	perception	perception	NOUN
fcis-25145	17	33	ability	ability	NOUN
fcis-25145	17	34	[	[	X
fcis-25145	17	35	2	2	NUM
fcis-25145	17	36	]	]	PUNCT
fcis-25145	17	37	.	.	PUNCT
fcis-25145	18	1	li	li	PROPN
fcis-25145	18	2	jing	jing	PROPN
fcis-25145	18	3	et	et	PROPN
fcis-25145	18	4	al	al	PROPN
fcis-25145	18	5	.	.	PROPN
fcis-25145	18	6	developed	develop	VERB
fcis-25145	18	7	a	a	DET
fcis-25145	18	8	multi	multi	ADJ
fcis-25145	18	9	-	-	ADJ
fcis-25145	18	10	scale	scale	ADJ
fcis-25145	18	11	network	network	NOUN
fcis-25145	18	12	model	model	NOUN
fcis-25145	18	13	for	for	ADP
fcis-25145	18	14	extracting	extract	VERB
fcis-25145	18	15	global	global	ADJ
fcis-25145	18	16	facial	facial	ADJ
fcis-25145	18	17	expression	expression	NOUN
fcis-25145	18	18	features	feature	VERB
fcis-25145	18	19	to	to	PART
fcis-25145	18	20	address	address	VERB
fcis-25145	18	21	the	the	DET
fcis-25145	18	22	issue	issue	NOUN
fcis-25145	18	23	of	of	ADP
fcis-25145	18	24	facial	facial	ADJ
fcis-25145	18	25	occlusion	occlusion	NOUN
fcis-25145	18	26	in	in	ADP
fcis-25145	18	27	natural	natural	ADJ
fcis-25145	18	28	environments	environment	NOUN
fcis-25145	18	29	,	,	PUNCT
fcis-25145	18	30	which	which	PRON
fcis-25145	18	31	improved	improve	VERB
fcis-25145	18	32	the	the	DET
fcis-25145	18	33	performance	performance	NOUN
fcis-25145	18	34	of	of	ADP
fcis-25145	18	35	facial	facial	ADJ
fcis-25145	18	36	expression	expression	NOUN
fcis-25145	18	37	recognition	recognition	NOUN
fcis-25145	18	38	in	in	ADP
fcis-25145	18	39	natural	natural	ADJ
fcis-25145	18	40	environments	environment	NOUN
fcis-25145	18	41	[	[	X
fcis-25145	18	42	3	3	NUM
fcis-25145	18	43	]	]	PUNCT
fcis-25145	18	44	.	.	PUNCT
fcis-25145	19	1	li	li	PROPN
fcis-25145	19	2	chunhong	chunhong	PROPN
fcis-25145	19	3	et	et	PROPN
fcis-25145	19	4	al	al	PROPN
fcis-25145	19	5	.	.	PROPN
fcis-25145	19	6	constructed	construct	VERB
fcis-25145	19	7	a	a	DET
fcis-25145	19	8	segmentation	segmentation	NOUN
fcis-25145	19	9	network	network	NOUN
fcis-25145	19	10	to	to	PART
fcis-25145	19	11	extract	extract	VERB
fcis-25145	19	12	important	important	ADJ
fcis-25145	19	13	facial	facial	ADJ
fcis-25145	19	14	features	feature	NOUN
fcis-25145	19	15	for	for	ADP
fcis-25145	19	16	expression	expression	NOUN
fcis-25145	19	17	recognition	recognition	NOUN
fcis-25145	19	18	,	,	PUNCT
fcis-25145	19	19	and	and	CCONJ
fcis-25145	19	20	then	then	ADV
fcis-25145	19	21	constructed	construct	VERB
fcis-25145	19	22	a	a	DET
fcis-25145	19	23	base	base	NOUN
fcis-25145	19	24	classifier	classifier	NOUN
fcis-25145	19	25	to	to	PART
fcis-25145	19	26	extract	extract	VERB
fcis-25145	19	27	different	different	ADJ
fcis-25145	19	28	levels	level	NOUN
fcis-25145	19	29	of	of	ADP
fcis-25145	19	30	expression	expression	NOUN
fcis-25145	19	31	features	feature	NOUN
fcis-25145	19	32	.	.	PUNCT
fcis-25145	20	1	experimental	experimental	ADJ
fcis-25145	20	2	verification	verification	NOUN
fcis-25145	20	3	showed	show	VERB
fcis-25145	20	4	that	that	SCONJ
fcis-25145	20	5	it	it	PRON
fcis-25145	20	6	can	can	AUX
fcis-25145	20	7	effectively	effectively	ADV
fcis-25145	20	8	improve	improve	VERB
fcis-25145	20	9	the	the	DET
fcis-25145	20	10	expression	expression	NOUN
fcis-25145	20	11	recognition	recognition	NOUN
fcis-25145	20	12	rate	rate	NOUN
fcis-25145	20	13	[	[	X
fcis-25145	20	14	4	4	NUM
fcis-25145	20	15	]	]	PUNCT
fcis-25145	20	16	.	.	PUNCT
fcis-25145	21	1	liu	liu	PROPN
fcis-25145	21	2	jin	jin	PROPN
fcis-25145	21	3	et	et	PROPN
fcis-25145	21	4	al	al	PROPN
fcis-25145	21	5	.	.	PROPN
fcis-25145	21	6	proposed	propose	VERB
fcis-25145	21	7	a	a	DET
fcis-25145	21	8	deep	deep	ADJ
fcis-25145	21	9	convolutional	convolutional	ADJ
fcis-25145	21	10	residual	residual	ADJ
fcis-25145	21	11	network	network	NOUN
fcis-25145	21	12	module	module	NOUN
fcis-25145	21	13	to	to	PART
fcis-25145	21	14	extract	extract	VERB
fcis-25145	21	15	features	feature	NOUN
fcis-25145	21	16	,	,	PUNCT
fcis-25145	21	17	which	which	PRON
fcis-25145	21	18	can	can	AUX
fcis-25145	21	19	effectively	effectively	ADV
fcis-25145	21	20	extract	extract	VERB
fcis-25145	21	21	subtle	subtle	ADJ
fcis-25145	21	22	facial	facial	ADJ
fcis-25145	21	23	features	feature	NOUN
fcis-25145	21	24	by	by	ADP
fcis-25145	21	25	fusing	fuse	VERB
fcis-25145	21	26	with	with	ADP
fcis-25145	21	27	global	global	ADJ
fcis-25145	21	28	features	feature	NOUN
fcis-25145	21	29	,	,	PUNCT
fcis-25145	21	30	improving	improve	VERB
fcis-25145	21	31	the	the	DET
fcis-25145	21	32	discriminative	discriminative	NOUN
fcis-25145	21	33	ability	ability	NOUN
fcis-25145	21	34	of	of	ADP
fcis-25145	21	35	facial	facial	ADJ
fcis-25145	21	36	expression	expression	NOUN
fcis-25145	21	37	changes	change	NOUN
fcis-25145	21	38	[	[	X
fcis-25145	21	39	5	5	NUM
fcis-25145	21	40	]	]	PUNCT
fcis-25145	21	41	.	.	PUNCT
fcis-25145	22	1	in	in	ADP
fcis-25145	22	2	order	order	NOUN
fcis-25145	22	3	to	to	PART
fcis-25145	22	4	solve	solve	VERB
fcis-25145	22	5	the	the	DET
fcis-25145	22	6	problem	problem	NOUN
fcis-25145	22	7	of	of	ADP
fcis-25145	22	8	the	the	DET
fcis-25145	22	9	large	large	ADJ
fcis-25145	22	10	number	number	NOUN
fcis-25145	22	11	of	of	ADP
fcis-25145	22	12	network	network	NOUN
fcis-25145	22	13	parameters	parameter	NOUN
fcis-25145	22	14	that	that	PRON
fcis-25145	22	15	can	can	AUX
fcis-25145	22	16	not	not	PART
fcis-25145	22	17	extract	extract	VERB
fcis-25145	22	18	a	a	DET
fcis-25145	22	19	large	large	ADJ
fcis-25145	22	20	number	number	NOUN
fcis-25145	22	21	of	of	ADP
fcis-25145	22	22	effective	effective	ADJ
fcis-25145	22	23	high	high	ADJ
fcis-25145	22	24	-	-	PUNCT
fcis-25145	22	25	dimensional	dimensional	ADJ
fcis-25145	22	26	facial	facial	ADJ
fcis-25145	22	27	expression	expression	NOUN
fcis-25145	22	28	features	feature	NOUN
fcis-25145	22	29	,	,	PUNCT
fcis-25145	22	30	this	this	DET
fcis-25145	22	31	paper	paper	NOUN
fcis-25145	22	32	improves	improve	VERB
fcis-25145	22	33	the	the	DET
fcis-25145	22	34	deep	deep	ADJ
fcis-25145	22	35	convolutional	convolutional	ADJ
fcis-25145	22	36	neural	neural	ADJ
fcis-25145	22	37	network	network	NOUN
fcis-25145	22	38	xception	xception	NOUN
fcis-25145	22	39	architecture	architecture	NOUN
fcis-25145	22	40	.	.	PUNCT
fcis-25145	23	1	the	the	DET
fcis-25145	23	2	core	core	NOUN
fcis-25145	23	3	of	of	ADP
fcis-25145	23	4	the	the	DET
fcis-25145	23	5	network	network	NOUN
fcis-25145	23	6	module	module	NOUN
fcis-25145	23	7	is	be	AUX
fcis-25145	23	8	the	the	DET
fcis-25145	23	9	residual	residual	ADJ
fcis-25145	23	10	module	module	NOUN
fcis-25145	23	11	and	and	CCONJ
fcis-25145	23	12	depthwise	depthwise	NOUN
fcis-25145	23	13	separable	separable	ADJ
fcis-25145	23	14	convolution	convolution	NOUN
fcis-25145	23	15	.	.	PUNCT
fcis-25145	24	1	at	at	ADP
fcis-25145	24	2	the	the	DET
fcis-25145	24	3	same	same	ADJ
fcis-25145	24	4	time	time	NOUN
fcis-25145	24	5	,	,	PUNCT
fcis-25145	24	6	the	the	DET
fcis-25145	24	7	relu6	relu6	PROPN
fcis-25145	24	8	activation	activation	NOUN
fcis-25145	24	9	function	function	NOUN
fcis-25145	24	10	is	be	AUX
fcis-25145	24	11	used	use	VERB
fcis-25145	24	12	to	to	PART
fcis-25145	24	13	input	input	VERB
fcis-25145	24	14	images	image	NOUN
fcis-25145	24	15	into	into	ADP
fcis-25145	24	16	the	the	DET
fcis-25145	24	17	improved	improved	ADJ
fcis-25145	24	18	network	network	NOUN
fcis-25145	24	19	model	model	NOUN
fcis-25145	24	20	for	for	ADP
fcis-25145	24	21	multiple	multiple	ADJ
fcis-25145	24	22	training	training	NOUN
fcis-25145	24	23	experiments	experiment	NOUN
fcis-25145	24	24	.	.	PUNCT
fcis-25145	25	1	the	the	DET
fcis-25145	25	2	experimental	experimental	ADJ
fcis-25145	25	3	results	result	NOUN
fcis-25145	25	4	show	show	VERB
fcis-25145	25	5	that	that	SCONJ
fcis-25145	25	6	the	the	DET
fcis-25145	25	7	improved	improved	ADJ
fcis-25145	25	8	model	model	NOUN
fcis-25145	25	9	has	have	AUX
fcis-25145	25	10	achieved	achieve	VERB
fcis-25145	25	11	certain	certain	ADJ
fcis-25145	25	12	facial	facial	ADJ
fcis-25145	25	13	expression	expression	NOUN
fcis-25145	25	14	recognition	recognition	NOUN
fcis-25145	25	15	effects	effect	NOUN
fcis-25145	25	16	.	.	PUNCT
fcis-25145	26	1	2	2	X
fcis-25145	26	2	.	.	X
fcis-25145	26	3	depthwise	depthwise	VERB
fcis-25145	26	4	separable	separable	ADJ
fcis-25145	26	5	convolution	convolution	NOUN
fcis-25145	26	6	due	due	ADP
fcis-25145	26	7	to	to	ADP
fcis-25145	26	8	the	the	DET
fcis-25145	26	9	fact	fact	NOUN
fcis-25145	26	10	that	that	SCONJ
fcis-25145	26	11	the	the	DET
fcis-25145	26	12	conventional	conventional	ADJ
fcis-25145	26	13	convolution	convolution	NOUN
fcis-25145	26	14	in	in	ADP
fcis-25145	26	15	general	general	ADJ
fcis-25145	26	16	network	network	NOUN
fcis-25145	26	17	structures	structure	NOUN
fcis-25145	26	18	processes	process	VERB
fcis-25145	26	19	all	all	DET
fcis-25145	26	20	channels	channel	NOUN
fcis-25145	26	21	with	with	ADP
fcis-25145	26	22	a	a	DET
fcis-25145	26	23	single	single	ADJ
fcis-25145	26	24	convolution	convolution	NOUN
fcis-25145	26	25	kernel	kernel	NOUN
fcis-25145	26	26	,	,	PUNCT
fcis-25145	26	27	which	which	PRON
fcis-25145	26	28	not	not	PART
fcis-25145	26	29	only	only	ADV
fcis-25145	26	30	increases	increase	VERB
fcis-25145	26	31	the	the	DET
fcis-25145	26	32	number	number	NOUN
fcis-25145	26	33	of	of	ADP
fcis-25145	26	34	parameters	parameter	NOUN
fcis-25145	26	35	but	but	CCONJ
fcis-25145	26	36	also	also	ADV
fcis-25145	26	37	has	have	VERB
fcis-25145	26	38	poor	poor	ADJ
fcis-25145	26	39	performance	performance	NOUN
fcis-25145	26	40	in	in	ADP
fcis-25145	26	41	extracting	extract	VERB
fcis-25145	26	42	facial	facial	ADJ
fcis-25145	26	43	features	feature	NOUN
fcis-25145	26	44	,	,	PUNCT
fcis-25145	26	45	the	the	DET
fcis-25145	26	46	depth	depth	NOUN
fcis-25145	26	47	separable	separable	NOUN
fcis-25145	26	48	convolution	convolution	NOUN
fcis-25145	26	49	used	use	VERB
fcis-25145	26	50	in	in	ADP
fcis-25145	26	51	this	this	DET
fcis-25145	26	52	paper	paper	NOUN
fcis-25145	26	53	processes	process	VERB
fcis-25145	26	54	one	one	NUM
fcis-25145	26	55	channel	channel	NOUN
fcis-25145	26	56	with	with	ADP
fcis-25145	26	57	a	a	DET
fcis-25145	26	58	single	single	ADJ
fcis-25145	26	59	convolution	convolution	NOUN
fcis-25145	26	60	kernel	kernel	NOUN
fcis-25145	26	61	.	.	PUNCT
fcis-25145	27	1	the	the	DET
fcis-25145	27	2	idea	idea	NOUN
fcis-25145	27	3	of	of	ADP
fcis-25145	27	4	this	this	DET
fcis-25145	27	5	article	article	NOUN
fcis-25145	27	6	is	be	AUX
fcis-25145	27	7	to	to	PART
fcis-25145	27	8	first	first	ADV
fcis-25145	27	9	perform	perform	VERB
fcis-25145	27	10	convolution	convolution	NOUN
fcis-25145	27	11	operations	operation	NOUN
fcis-25145	27	12	on	on	ADP
fcis-25145	27	13	the	the	DET
fcis-25145	27	14	channel	channel	NOUN
fcis-25145	27	15	dimension	dimension	NOUN
fcis-25145	27	16	(	(	PUNCT
fcis-25145	27	17	1x1	1x1	NUM
fcis-25145	27	18	convolution	convolution	NOUN
fcis-25145	27	19	)	)	PUNCT
fcis-25145	27	20	,	,	PUNCT
fcis-25145	27	21	and	and	CCONJ
fcis-25145	27	22	then	then	ADV
fcis-25145	27	23	perform	perform	VERB
fcis-25145	27	24	convolution	convolution	NOUN
fcis-25145	27	25	operations	operation	NOUN
fcis-25145	27	26	on	on	ADP
fcis-25145	27	27	each	each	DET
fcis-25145	27	28	dimension	dimension	NOUN
fcis-25145	27	29	in	in	ADP
fcis-25145	27	30	the	the	DET
fcis-25145	27	31	spatial	spatial	ADJ
fcis-25145	27	32	dimension	dimension	NOUN
fcis-25145	27	33	(	(	PUNCT
fcis-25145	27	34	3x3	3x3	NUM
fcis-25145	27	35	convolution	convolution	NOUN
fcis-25145	27	36	)	)	PUNCT
fcis-25145	28	1	[	[	X
fcis-25145	28	2	6,7	6,7	NUM
fcis-25145	28	3	]	]	PUNCT
fcis-25145	28	4	,	,	PUNCT
fcis-25145	28	5	using	use	VERB
fcis-25145	28	6	the	the	DET
fcis-25145	28	7	activation	activation	NOUN
fcis-25145	28	8	function	function	NOUN
fcis-25145	28	9	relu6	relu6	PROPN
fcis-25145	28	10	.	.	PUNCT
fcis-25145	29	1	in	in	ADP
fcis-25145	29	2	order	order	NOUN
fcis-25145	29	3	to	to	PART
fcis-25145	29	4	ensure	ensure	VERB
fcis-25145	29	5	that	that	SCONJ
fcis-25145	29	6	the	the	DET
fcis-25145	29	7	data	data	NOUN
fcis-25145	29	8	is	be	AUX
fcis-25145	29	9	not	not	PART
fcis-25145	29	10	corrupted	corrupt	VERB
fcis-25145	29	11	,	,	PUNCT
fcis-25145	29	12	no	no	DET
fcis-25145	29	13	activation	activation	NOUN
fcis-25145	29	14	function	function	NOUN
fcis-25145	29	15	layer	layer	NOUN
fcis-25145	29	16	is	be	AUX
fcis-25145	29	17	added	add	VERB
fcis-25145	29	18	,	,	PUNCT
fcis-25145	29	19	and	and	CCONJ
fcis-25145	29	20	the	the	DET
fcis-25145	29	21	activation	activation	NOUN
fcis-25145	29	22	function	function	NOUN
fcis-25145	29	23	relu6	relu6	PROPN
fcis-25145	29	24	is	be	AUX
fcis-25145	29	25	used	use	VERB
fcis-25145	29	26	for	for	ADP
fcis-25145	29	27	other	other	ADJ
fcis-25145	29	28	layers	layer	NOUN
fcis-25145	30	1	[	[	X
fcis-25145	30	2	8	8	NUM
fcis-25145	30	3	]	]	PUNCT
fcis-25145	30	4	.	.	PUNCT
fcis-25145	31	1	this	this	DET
fcis-25145	31	2	network	network	NOUN
fcis-25145	31	3	module	module	NOUN
fcis-25145	31	4	achieves	achieve	VERB
fcis-25145	31	5	decoupling	decouple	VERB
fcis-25145	31	6	by	by	ADP
fcis-25145	31	7	separating	separate	VERB
fcis-25145	31	8	the	the	DET
fcis-25145	31	9	channel	channel	NOUN
fcis-25145	31	10	dimension	dimension	NOUN
fcis-25145	31	11	and	and	CCONJ
fcis-25145	31	12	spatial	spatial	ADJ
fcis-25145	31	13	dimension	dimension	NOUN
fcis-25145	31	14	,	,	PUNCT
fcis-25145	31	15	making	make	VERB
fcis-25145	31	16	the	the	DET
fcis-25145	31	17	entire	entire	ADJ
fcis-25145	31	18	process	process	NOUN
fcis-25145	31	19	more	more	ADV
fcis-25145	31	20	efficient	efficient	ADJ
fcis-25145	31	21	.	.	PUNCT
fcis-25145	32	1	when	when	SCONJ
fcis-25145	32	2	building	build	VERB
fcis-25145	32	3	the	the	DET
fcis-25145	32	4	model	model	NOUN
fcis-25145	32	5	,	,	PUNCT
fcis-25145	32	6	all	all	DET
fcis-25145	32	7	76	76	NUM
fcis-25145	32	8	separable	separable	ADJ
fcis-25145	32	9	convolutional	convolutional	ADJ
fcis-25145	32	10	layers	layer	NOUN
fcis-25145	32	11	are	be	AUX
fcis-25145	32	12	followed	follow	VERB
fcis-25145	32	13	by	by	ADP
fcis-25145	32	14	batch	batch	NOUN
fcis-25145	32	15	normalization	normalization	NOUN
fcis-25145	32	16	,	,	PUNCT
fcis-25145	32	17	and	and	CCONJ
fcis-25145	32	18	a	a	DET
fcis-25145	32	19	depth	depth	NOUN
fcis-25145	32	20	multiplier	multipli	ADJ
fcis-25145	32	21	of	of	ADP
fcis-25145	32	22	1	1	NUM
fcis-25145	32	23	is	be	AUX
fcis-25145	32	24	used	use	VERB
fcis-25145	32	25	.	.	PUNCT
fcis-25145	33	1	the	the	DET
fcis-25145	33	2	separableconv2d	separableconv2d	ADJ
fcis-25145	33	3	layer	layer	NOUN
fcis-25145	33	4	in	in	ADP
fcis-25145	33	5	keras	keras	PROPN
fcis-25145	33	6	can	can	AUX
fcis-25145	33	7	establish	establish	VERB
fcis-25145	33	8	corresponding	corresponding	ADJ
fcis-25145	33	9	functionality	functionality	NOUN
fcis-25145	33	10	.	.	PUNCT
fcis-25145	34	1	as	as	SCONJ
fcis-25145	34	2	shown	show	VERB
fcis-25145	34	3	in	in	ADP
fcis-25145	34	4	fig	fig	NOUN
fcis-25145	34	5	.	.	PUNCT
fcis-25145	35	1	1	1	X
fcis-25145	35	2	.	.	X
fcis-25145	35	3	fig	fig	NOUN
fcis-25145	35	4	1	1	NUM
fcis-25145	35	5	.	.	PUNCT
fcis-25145	36	1	depthwise	depthwise	VERB
fcis-25145	36	2	separable	separable	ADJ
fcis-25145	36	3	convolution	convolution	NOUN
fcis-25145	36	4	structure	structure	NOUN
fcis-25145	36	5	diagram	diagram	NOUN
fcis-25145	36	6	3	3	NUM
fcis-25145	36	7	.	.	PUNCT
fcis-25145	37	1	deep	deep	ADJ
fcis-25145	37	2	residual	residual	ADJ
fcis-25145	37	3	network	network	NOUN
fcis-25145	37	4	fig	fig	NOUN
fcis-25145	37	5	2	2	NUM
fcis-25145	37	6	.	.	PUNCT
fcis-25145	37	7	residual	residual	ADJ
fcis-25145	37	8	1	1	NUM
fcis-25145	37	9	and	and	CCONJ
fcis-25145	37	10	residual	residual	ADJ
fcis-25145	37	11	2	2	NUM
fcis-25145	37	12	in	in	ADP
fcis-25145	37	13	order	order	NOUN
fcis-25145	37	14	to	to	PART
fcis-25145	37	15	overcome	overcome	VERB
fcis-25145	37	16	the	the	DET
fcis-25145	37	17	problems	problem	NOUN
fcis-25145	37	18	of	of	ADP
fcis-25145	37	19	gradient	gradient	NOUN
fcis-25145	37	20	vanishing	vanishing	NOUN
fcis-25145	37	21	and	and	CCONJ
fcis-25145	37	22	decreased	decrease	VERB
fcis-25145	37	23	learning	learn	VERB
fcis-25145	37	24	efficiency	efficiency	NOUN
fcis-25145	37	25	caused	cause	VERB
fcis-25145	37	26	by	by	ADP
fcis-25145	37	27	the	the	DET
fcis-25145	37	28	deepening	deepening	NOUN
fcis-25145	37	29	of	of	ADP
fcis-25145	37	30	the	the	DET
fcis-25145	37	31	network	network	NOUN
fcis-25145	37	32	,	,	PUNCT
fcis-25145	37	33	this	this	DET
fcis-25145	37	34	paper	paper	NOUN
fcis-25145	37	35	uses	use	VERB
fcis-25145	37	36	residual	residual	ADJ
fcis-25145	37	37	modules	module	NOUN
fcis-25145	37	38	to	to	PART
fcis-25145	37	39	construct	construct	VERB
fcis-25145	37	40	the	the	DET
fcis-25145	37	41	network	network	NOUN
fcis-25145	37	42	structure	structure	NOUN
fcis-25145	37	43	.	.	PUNCT
fcis-25145	38	1	residual	residual	ADJ
fcis-25145	38	2	network	network	NOUN
fcis-25145	38	3	is	be	AUX
fcis-25145	38	4	a	a	DET
fcis-25145	38	5	method	method	NOUN
fcis-25145	38	6	of	of	ADP
fcis-25145	38	7	directly	directly	ADV
fcis-25145	38	8	skipping	skip	VERB
fcis-25145	38	9	multiple	multiple	ADJ
fcis-25145	38	10	layers	layer	NOUN
fcis-25145	38	11	and	and	CCONJ
fcis-25145	38	12	introducing	introduce	VERB
fcis-25145	38	13	the	the	DET
fcis-25145	38	14	data	data	NOUN
fcis-25145	38	15	output	output	NOUN
fcis-25145	38	16	from	from	ADP
fcis-25145	38	17	one	one	NUM
fcis-25145	38	18	of	of	ADP
fcis-25145	38	19	the	the	DET
fcis-25145	38	20	previous	previous	ADJ
fcis-25145	38	21	layers	layer	NOUN
fcis-25145	38	22	into	into	ADP
fcis-25145	38	23	the	the	DET
fcis-25145	38	24	input	input	NOUN
fcis-25145	38	25	part	part	NOUN
fcis-25145	38	26	of	of	ADP
fcis-25145	38	27	the	the	DET
fcis-25145	38	28	subsequent	subsequent	ADJ
fcis-25145	38	29	layers	layer	NOUN
fcis-25145	38	30	.	.	PUNCT
fcis-25145	39	1	the	the	DET
fcis-25145	39	2	difference	difference	NOUN
fcis-25145	39	3	between	between	ADP
fcis-25145	39	4	residual	residual	ADJ
fcis-25145	39	5	networks	network	NOUN
fcis-25145	39	6	and	and	CCONJ
fcis-25145	39	7	ordinary	ordinary	ADJ
fcis-25145	39	8	networks	network	NOUN
fcis-25145	39	9	is	be	AUX
fcis-25145	39	10	the	the	DET
fcis-25145	39	11	addition	addition	NOUN
fcis-25145	39	12	of	of	ADP
fcis-25145	39	13	a	a	DET
fcis-25145	39	14	shortcut	shortcut	NOUN
fcis-25145	39	15	branch	branch	NOUN
fcis-25145	39	16	,	,	PUNCT
fcis-25145	39	17	which	which	PRON
fcis-25145	39	18	allows	allow	VERB
fcis-25145	39	19	the	the	DET
fcis-25145	39	20	loss	loss	NOUN
fcis-25145	39	21	of	of	ADP
fcis-25145	39	22	the	the	DET
fcis-25145	39	23	network	network	NOUN
fcis-25145	39	24	during	during	ADP
fcis-25145	39	25	backpropagation	backpropagation	NOUN
fcis-25145	39	26	training	training	NOUN
fcis-25145	39	27	to	to	PART
fcis-25145	39	28	be	be	AUX
fcis-25145	39	29	directly	directly	ADV
fcis-25145	39	30	transmitted	transmit	VERB
fcis-25145	39	31	to	to	ADP
fcis-25145	39	32	the	the	DET
fcis-25145	39	33	earlier	early	ADJ
fcis-25145	39	34	network	network	NOUN
fcis-25145	39	35	through	through	ADP
fcis-25145	39	36	this	this	DET
fcis-25145	39	37	shortcut	shortcut	NOUN
fcis-25145	39	38	,	,	PUNCT
fcis-25145	39	39	thereby	thereby	ADV
fcis-25145	39	40	slowing	slow	VERB
fcis-25145	39	41	down	down	ADP
fcis-25145	39	42	the	the	DET
fcis-25145	39	43	problem	problem	NOUN
fcis-25145	39	44	of	of	ADP
fcis-25145	39	45	network	network	NOUN
fcis-25145	39	46	degradation	degradation	NOUN
fcis-25145	39	47	[	[	X
fcis-25145	39	48	9	9	NUM
fcis-25145	39	49	]	]	PUNCT
fcis-25145	39	50	.	.	PUNCT
fcis-25145	40	1	as	as	SCONJ
fcis-25145	40	2	shown	show	VERB
fcis-25145	40	3	in	in	ADP
fcis-25145	40	4	fig	fig	NOUN
fcis-25145	40	5	.	.	PUNCT
fcis-25145	41	1	2	2	NUM
fcis-25145	41	2	,	,	PUNCT
fcis-25145	41	3	it	it	PRON
fcis-25145	41	4	is	be	AUX
fcis-25145	41	5	a	a	DET
fcis-25145	41	6	linear	linear	ADJ
fcis-25145	41	7	stack	stack	NOUN
fcis-25145	41	8	of	of	ADP
fcis-25145	41	9	depthwise	depthwise	NOUN
fcis-25145	41	10	separable	separable	ADJ
fcis-25145	41	11	convolutional	convolutional	ADJ
fcis-25145	41	12	layers	layer	NOUN
fcis-25145	41	13	with	with	ADP
fcis-25145	41	14	residual	residual	ADJ
fcis-25145	41	15	connections	connection	NOUN
fcis-25145	41	16	.	.	PUNCT
fcis-25145	42	1	adding	add	VERB
fcis-25145	42	2	residual	residual	ADJ
fcis-25145	42	3	structures	structure	NOUN
fcis-25145	42	4	to	to	ADP
fcis-25145	42	5	separable	separable	ADJ
fcis-25145	42	6	convolutions	convolution	NOUN
fcis-25145	42	7	can	can	AUX
fcis-25145	42	8	make	make	VERB
fcis-25145	42	9	the	the	DET
fcis-25145	42	10	network	network	NOUN
fcis-25145	42	11	converge	converge	VERB
fcis-25145	42	12	faster	fast	ADV
fcis-25145	42	13	and	and	CCONJ
fcis-25145	42	14	more	more	ADV
fcis-25145	42	15	accurately	accurately	ADV
fcis-25145	42	16	.	.	PUNCT
fcis-25145	43	1	4	4	X
fcis-25145	43	2	.	.	X
fcis-25145	43	3	network	network	NOUN
fcis-25145	43	4	structure	structure	NOUN
fcis-25145	43	5	and	and	CCONJ
fcis-25145	43	6	experimental	experimental	ADJ
fcis-25145	43	7	analysis	analysis	NOUN
fcis-25145	43	8	table	table	NOUN
fcis-25145	43	9	1	1	NUM
fcis-25145	43	10	.	.	PUNCT
fcis-25145	44	1	recognition	recognition	NOUN
fcis-25145	44	2	results	result	NOUN
fcis-25145	44	3	of	of	ADP
fcis-25145	44	4	ck+dataset	ck+dataset	NOUN
fcis-25145	44	5	on	on	ADP
fcis-25145	44	6	different	different	ADJ
fcis-25145	44	7	network	network	NOUN
fcis-25145	44	8	models	model	NOUN
fcis-25145	44	9	numble	numble	ADJ
fcis-25145	44	10	methods	method	NOUN
fcis-25145	44	11	ck+	ck+	NOUN
fcis-25145	44	12	accuracy	accuracy	NOUN
fcis-25145	44	13	1	1	NUM
fcis-25145	44	14	xception	xception	NOUN
fcis-25145	44	15	87.24	87.24	NUM
fcis-25145	44	16	%	%	NOUN
fcis-25145	44	17	2	2	NUM
fcis-25145	44	18	inceptionv4	inceptionv4	NOUN
fcis-25145	44	19	89.92	89.92	NUM
fcis-25145	44	20	%	%	NOUN
fcis-25145	44	21	3	3	NUM
fcis-25145	44	22	our	our	PRON
fcis-25145	44	23	95.1	95.1	NUM
fcis-25145	44	24	%	%	NOUN
fcis-25145	44	25	the	the	DET
fcis-25145	44	26	xception	xception	PROPN
fcis-25145	44	27	algorithm	algorithm	PROPN
fcis-25145	44	28	was	be	AUX
fcis-25145	44	29	proposed	propose	VERB
fcis-25145	44	30	by	by	ADP
fcis-25145	44	31	a	a	DET
fcis-25145	44	32	research	research	NOUN
fcis-25145	44	33	team	team	NOUN
fcis-25145	44	34	from	from	ADP
fcis-25145	44	35	google	google	PROPN
fcis-25145	44	36	in	in	ADP
fcis-25145	44	37	2016	2016	NUM
fcis-25145	44	38	.	.	PUNCT
fcis-25145	45	1	this	this	DET
fcis-25145	45	2	article	article	NOUN
fcis-25145	45	3	improves	improve	VERB
fcis-25145	45	4	the	the	DET
fcis-25145	45	5	model	model	NOUN
fcis-25145	45	6	structure	structure	NOUN
fcis-25145	45	7	based	base	VERB
fcis-25145	45	8	on	on	ADP
fcis-25145	45	9	this	this	DET
fcis-25145	45	10	model	model	NOUN
fcis-25145	45	11	and	and	CCONJ
fcis-25145	45	12	conducts	conduct	VERB
fcis-25145	45	13	training	training	NOUN
fcis-25145	45	14	and	and	CCONJ
fcis-25145	45	15	prediction	prediction	NOUN
fcis-25145	45	16	experiments	experiment	NOUN
fcis-25145	45	17	on	on	ADP
fcis-25145	45	18	facial	facial	ADJ
fcis-25145	45	19	expression	expression	NOUN
fcis-25145	45	20	images	image	NOUN
fcis-25145	45	21	.	.	PUNCT
fcis-25145	46	1	the	the	DET
fcis-25145	46	2	overall	overall	ADJ
fcis-25145	46	3	structure	structure	NOUN
fcis-25145	46	4	of	of	ADP
fcis-25145	46	5	the	the	DET
fcis-25145	46	6	network	network	NOUN
fcis-25145	46	7	is	be	AUX
fcis-25145	46	8	first	first	ADV
fcis-25145	46	9	passed	pass	VERB
fcis-25145	46	10	through	through	ADP
fcis-25145	46	11	two	two	NUM
fcis-25145	46	12	conv2d	conv2d	ADJ
fcis-25145	46	13	convolutional	convolutional	ADJ
fcis-25145	46	14	layers	layer	NOUN
fcis-25145	46	15	,	,	PUNCT
fcis-25145	46	16	with	with	ADP
fcis-25145	46	17	32	32	NUM
fcis-25145	46	18	and	and	CCONJ
fcis-25145	46	19	64	64	NUM
fcis-25145	46	20	channels	channel	NOUN
fcis-25145	46	21	respectively	respectively	ADV
fcis-25145	46	22	,	,	PUNCT
fcis-25145	46	23	a	a	DET
fcis-25145	46	24	convolution	convolution	NOUN
fcis-25145	46	25	kernel	kernel	NOUN
fcis-25145	46	26	size	size	NOUN
fcis-25145	46	27	of	of	ADP
fcis-25145	46	28	3	3	NUM
fcis-25145	46	29	×	×	NOUN
fcis-25145	46	30	3	3	NUM
fcis-25145	46	31	,	,	PUNCT
fcis-25145	46	32	and	and	CCONJ
fcis-25145	46	33	a	a	DET
fcis-25145	46	34	stride	stride	NOUN
fcis-25145	46	35	of	of	ADP
fcis-25145	46	36	1	1	NUM
fcis-25145	46	37	;	;	PUNCT
fcis-25145	46	38	then	then	ADV
fcis-25145	46	39	,	,	PUNCT
fcis-25145	46	40	the	the	DET
fcis-25145	46	41	output	output	NOUN
fcis-25145	46	42	is	be	AUX
fcis-25145	46	43	passed	pass	VERB
fcis-25145	46	44	through	through	ADP
fcis-25145	46	45	residual	residual	ADJ
fcis-25145	46	46	1	1	NUM
fcis-25145	46	47	,	,	PUNCT
fcis-25145	46	48	residual	residual	ADJ
fcis-25145	46	49	2	2	NUM
fcis-25145	46	50	,	,	PUNCT
fcis-25145	46	51	residual	residual	ADJ
fcis-25145	46	52	1	1	NUM
fcis-25145	46	53	,	,	PUNCT
fcis-25145	46	54	residual	residual	ADJ
fcis-25145	46	55	2	2	NUM
fcis-25145	46	56	,	,	PUNCT
fcis-25145	46	57	residual	residual	ADJ
fcis-25145	46	58	2	2	NUM
fcis-25145	46	59	,	,	PUNCT
fcis-25145	46	60	residual	residual	ADJ
fcis-25145	46	61	1	1	NUM
fcis-25145	46	62	,	,	PUNCT
fcis-25145	46	63	and	and	CCONJ
fcis-25145	46	64	separableconv2d	separableconv2d	VERB
fcis-25145	46	65	,	,	PUNCT
fcis-25145	46	66	with	with	ADP
fcis-25145	46	67	channel	channel	NOUN
fcis-25145	46	68	numbers	number	NOUN
fcis-25145	46	69	of	of	ADP
fcis-25145	46	70	128	128	NUM
fcis-25145	46	71	,	,	PUNCT
fcis-25145	46	72	256	256	NUM
fcis-25145	46	73	,	,	PUNCT
fcis-25145	46	74	728	728	NUM
fcis-25145	46	75	,	,	PUNCT
fcis-25145	46	76	728	728	NUM
fcis-25145	46	77	,	,	PUNCT
fcis-25145	46	78	728	728	NUM
fcis-25145	46	79	,	,	PUNCT
fcis-25145	46	80	728	728	NUM
fcis-25145	46	81	,	,	PUNCT
fcis-25145	46	82	728	728	NUM
fcis-25145	46	83	.	.	PUNCT
fcis-25145	47	1	the	the	DET
fcis-25145	47	2	stride	stride	ADJ
fcis-25145	47	3	sizes	size	NOUN
fcis-25145	47	4	are	be	AUX
fcis-25145	47	5	2	2	NUM
fcis-25145	47	6	,	,	PUNCT
fcis-25145	47	7	1	1	NUM
fcis-25145	47	8	,	,	PUNCT
fcis-25145	47	9	2	2	NUM
fcis-25145	47	10	,	,	PUNCT
fcis-25145	47	11	1	1	NUM
fcis-25145	47	12	,	,	PUNCT
fcis-25145	47	13	1	1	NUM
fcis-25145	47	14	,	,	PUNCT
fcis-25145	47	15	2	2	NUM
fcis-25145	47	16	,	,	PUNCT
fcis-25145	47	17	and	and	CCONJ
fcis-25145	47	18	1	1	NUM
fcis-25145	47	19	,	,	PUNCT
fcis-25145	47	20	respectively	respectively	ADV
fcis-25145	47	21	;	;	PUNCT
fcis-25145	47	22	finally	finally	ADV
fcis-25145	47	23	,	,	PUNCT
fcis-25145	47	24	the	the	DET
fcis-25145	47	25	output	output	NOUN
fcis-25145	47	26	image	image	NOUN
fcis-25145	47	27	is	be	AUX
fcis-25145	47	28	fed	feed	VERB
fcis-25145	47	29	into	into	ADP
fcis-25145	47	30	globalaveragepooling2d	globalaveragepooling2d	PROPN
fcis-25145	47	31	and	and	CCONJ
fcis-25145	47	32	a	a	DET
fcis-25145	47	33	conv2d	conv2d	ADJ
fcis-25145	47	34	operation	operation	NOUN
fcis-25145	47	35	with	with	ADP
fcis-25145	47	36	channel	channel	NOUN
fcis-25145	47	37	7	7	NUM
fcis-25145	47	38	.	.	PUNCT
fcis-25145	48	1	the	the	DET
fcis-25145	48	2	convolution	convolution	NOUN
fcis-25145	48	3	kernel	kernel	PROPN
fcis-25145	48	4	size	size	NOUN
fcis-25145	48	5	is	be	AUX
fcis-25145	48	6	1	1	NUM
fcis-25145	48	7	×	×	NOUN
fcis-25145	48	8	1	1	NUM
fcis-25145	48	9	,	,	PUNCT
fcis-25145	48	10	resulting	result	VERB
fcis-25145	48	11	in	in	ADP
fcis-25145	48	12	a	a	DET
fcis-25145	48	13	feature	feature	NOUN
fcis-25145	48	14	vector	vector	NOUN
fcis-25145	48	15	of	of	ADP
fcis-25145	48	16	1	1	NUM
fcis-25145	48	17	×	×	NOUN
fcis-25145	48	18	1	1	NUM
fcis-25145	48	19	×	×	NOUN
fcis-25145	48	20	728	728	NUM
fcis-25145	48	21	.	.	PUNCT
fcis-25145	49	1	the	the	DET
fcis-25145	49	2	dataset	dataset	NOUN
fcis-25145	49	3	used	use	VERB
fcis-25145	49	4	in	in	ADP
fcis-25145	49	5	this	this	DET
fcis-25145	49	6	article	article	NOUN
fcis-25145	49	7	is	be	AUX
fcis-25145	49	8	ck+	ck+	NOUN
fcis-25145	49	9	,	,	PUNCT
fcis-25145	49	10	and	and	CCONJ
fcis-25145	49	11	the	the	DET
fcis-25145	49	12	selected	select	VERB
fcis-25145	49	13	expression	expression	NOUN
fcis-25145	49	14	types	type	NOUN
fcis-25145	49	15	are	be	AUX
fcis-25145	49	16	angry	angry	ADJ
fcis-25145	49	17	,	,	PUNCT
fcis-25145	49	18	neutral	neutral	ADJ
fcis-25145	49	19	,	,	PUNCT
fcis-25145	49	20	disgust	disgust	ADJ
fcis-25145	49	21	,	,	PUNCT
fcis-25145	49	22	scared	scared	ADJ
fcis-25145	49	23	,	,	PUNCT
fcis-25145	49	24	happy	happy	ADJ
fcis-25145	49	25	,	,	PUNCT
fcis-25145	49	26	sad	sad	ADJ
fcis-25145	49	27	,	,	PUNCT
fcis-25145	49	28	and	and	CCONJ
fcis-25145	49	29	surprised	surprised	ADJ
fcis-25145	49	30	.	.	PUNCT
fcis-25145	50	1	through	through	ADP
fcis-25145	50	2	multiple	multiple	ADJ
fcis-25145	50	3	experiments	experiment	NOUN
fcis-25145	50	4	,	,	PUNCT
fcis-25145	50	5	the	the	DET
fcis-25145	50	6	accuracy	accuracy	NOUN
fcis-25145	50	7	of	of	ADP
fcis-25145	50	8	facial	facial	ADJ
fcis-25145	50	9	expressions	expression	NOUN
fcis-25145	50	10	has	have	AUX
fcis-25145	50	11	reached	reach	VERB
fcis-25145	50	12	95.1	95.1	NUM
fcis-25145	50	13	%	%	NOUN
fcis-25145	50	14	.	.	PUNCT
fcis-25145	51	1	this	this	DET
fcis-25145	51	2	model	model	NOUN
fcis-25145	51	3	reduces	reduce	VERB
fcis-25145	51	4	the	the	DET
fcis-25145	51	5	number	number	NOUN
fcis-25145	51	6	of	of	ADP
fcis-25145	51	7	parameters	parameter	NOUN
fcis-25145	51	8	and	and	CCONJ
fcis-25145	51	9	calculations	calculation	NOUN
fcis-25145	51	10	,	,	PUNCT
fcis-25145	51	11	achieving	achieve	VERB
fcis-25145	51	12	a	a	DET
fcis-25145	51	13	certain	certain	ADJ
fcis-25145	51	14	level	level	NOUN
fcis-25145	51	15	of	of	ADP
fcis-25145	51	16	facial	facial	ADJ
fcis-25145	51	17	expression	expression	NOUN
fcis-25145	51	18	recognition	recognition	NOUN
fcis-25145	51	19	rate	rate	NOUN
fcis-25145	51	20	.	.	PUNCT
fcis-25145	52	1	as	as	SCONJ
fcis-25145	52	2	shown	show	VERB
fcis-25145	52	3	in	in	ADP
fcis-25145	52	4	table	table	NOUN
fcis-25145	52	5	1	1	NUM
fcis-25145	52	6	.	.	NOUN
fcis-25145	52	7	5	5	NUM
fcis-25145	52	8	.	.	X
fcis-25145	52	9	summary	summary	NOUN
fcis-25145	52	10	with	with	ADP
fcis-25145	52	11	the	the	DET
fcis-25145	52	12	continuous	continuous	ADJ
fcis-25145	52	13	advancement	advancement	NOUN
fcis-25145	52	14	of	of	ADP
fcis-25145	52	15	feature	feature	NOUN
fcis-25145	52	16	extraction	extraction	NOUN
fcis-25145	52	17	in	in	ADP
fcis-25145	52	18	77	77	NUM
fcis-25145	52	19	neural	neural	ADJ
fcis-25145	52	20	network	network	NOUN
fcis-25145	52	21	models	model	NOUN
fcis-25145	52	22	,	,	PUNCT
fcis-25145	52	23	network	network	NOUN
fcis-25145	52	24	models	model	NOUN
fcis-25145	52	25	with	with	ADP
fcis-25145	52	26	small	small	ADJ
fcis-25145	52	27	size	size	NOUN
fcis-25145	52	28	and	and	CCONJ
fcis-25145	52	29	few	few	ADJ
fcis-25145	52	30	parameters	parameter	NOUN
fcis-25145	52	31	are	be	AUX
fcis-25145	52	32	receiving	receive	VERB
fcis-25145	52	33	increasing	increase	VERB
fcis-25145	52	34	attention	attention	NOUN
fcis-25145	52	35	.	.	PUNCT
fcis-25145	53	1	due	due	ADP
fcis-25145	53	2	to	to	ADP
fcis-25145	53	3	the	the	DET
fcis-25145	53	4	fact	fact	NOUN
fcis-25145	53	5	that	that	SCONJ
fcis-25145	53	6	traditional	traditional	ADJ
fcis-25145	53	7	feature	feature	NOUN
fcis-25145	53	8	extraction	extraction	NOUN
fcis-25145	53	9	algorithms	algorithm	NOUN
fcis-25145	53	10	spend	spend	VERB
fcis-25145	53	11	a	a	DET
fcis-25145	53	12	lot	lot	NOUN
fcis-25145	53	13	of	of	ADP
fcis-25145	53	14	time	time	NOUN
fcis-25145	53	15	and	and	CCONJ
fcis-25145	53	16	can	can	AUX
fcis-25145	53	17	not	not	PART
fcis-25145	53	18	extract	extract	VERB
fcis-25145	53	19	a	a	DET
fcis-25145	53	20	large	large	ADJ
fcis-25145	53	21	number	number	NOUN
fcis-25145	53	22	of	of	ADP
fcis-25145	53	23	effective	effective	ADJ
fcis-25145	53	24	highdimensional	highdimensional	ADJ
fcis-25145	53	25	facial	facial	ADJ
fcis-25145	53	26	expression	expression	NOUN
fcis-25145	53	27	features	feature	NOUN
fcis-25145	53	28	,	,	PUNCT
fcis-25145	53	29	and	and	CCONJ
fcis-25145	53	30	traditional	traditional	ADJ
fcis-25145	53	31	convolutional	convolutional	ADJ
fcis-25145	53	32	network	network	NOUN
fcis-25145	53	33	models	model	NOUN
fcis-25145	53	34	have	have	VERB
fcis-25145	53	35	a	a	DET
fcis-25145	53	36	large	large	ADJ
fcis-25145	53	37	number	number	NOUN
fcis-25145	53	38	of	of	ADP
fcis-25145	53	39	parameters	parameter	NOUN
fcis-25145	53	40	and	and	CCONJ
fcis-25145	53	41	volume	volume	NOUN
fcis-25145	53	42	in	in	ADP
fcis-25145	53	43	facial	facial	ADJ
fcis-25145	53	44	expression	expression	NOUN
fcis-25145	53	45	recognition	recognition	NOUN
fcis-25145	53	46	,	,	PUNCT
fcis-25145	53	47	this	this	DET
fcis-25145	53	48	paper	paper	NOUN
fcis-25145	53	49	conducts	conduct	VERB
fcis-25145	53	50	experiments	experiment	NOUN
fcis-25145	53	51	by	by	ADP
fcis-25145	53	52	constructing	construct	VERB
fcis-25145	53	53	a	a	DET
fcis-25145	53	54	network	network	NOUN
fcis-25145	53	55	model	model	NOUN
fcis-25145	53	56	.	.	PUNCT
fcis-25145	54	1	the	the	DET
fcis-25145	54	2	core	core	ADJ
fcis-25145	54	3	operations	operation	NOUN
fcis-25145	54	4	of	of	ADP
fcis-25145	54	5	the	the	DET
fcis-25145	54	6	model	model	NOUN
fcis-25145	54	7	are	be	AUX
fcis-25145	54	8	residual	residual	ADJ
fcis-25145	54	9	module	module	NOUN
fcis-25145	54	10	and	and	CCONJ
fcis-25145	54	11	depthwise	depthwise	NOUN
fcis-25145	54	12	separable	separable	ADJ
fcis-25145	54	13	convolution	convolution	NOUN
fcis-25145	54	14	,	,	PUNCT
fcis-25145	54	15	using	use	VERB
fcis-25145	54	16	relu6	relu6	PROPN
fcis-25145	54	17	activation	activation	NOUN
fcis-25145	54	18	function	function	NOUN
fcis-25145	54	19	,	,	PUNCT
fcis-25145	54	20	and	and	CCONJ
fcis-25145	54	21	training	training	NOUN
fcis-25145	54	22	and	and	CCONJ
fcis-25145	54	23	testing	test	VERB
fcis-25145	54	24	the	the	DET
fcis-25145	54	25	improved	improved	ADJ
fcis-25145	54	26	model	model	NOUN
fcis-25145	54	27	using	use	VERB
fcis-25145	54	28	the	the	DET
fcis-25145	54	29	publicly	publicly	ADV
fcis-25145	54	30	available	available	ADJ
fcis-25145	54	31	dataset	dataset	NOUN
fcis-25145	54	32	ck+	ck+	NOUN
fcis-25145	54	33	.	.	PUNCT
fcis-25145	55	1	through	through	ADP
fcis-25145	55	2	multiple	multiple	ADJ
fcis-25145	55	3	training	training	NOUN
fcis-25145	55	4	experiments	experiment	NOUN
fcis-25145	55	5	,	,	PUNCT
fcis-25145	55	6	the	the	DET
fcis-25145	55	7	results	result	NOUN
fcis-25145	55	8	show	show	VERB
fcis-25145	55	9	that	that	SCONJ
fcis-25145	55	10	the	the	DET
fcis-25145	55	11	improved	improved	ADJ
fcis-25145	55	12	model	model	NOUN
fcis-25145	55	13	has	have	AUX
fcis-25145	55	14	achieved	achieve	VERB
fcis-25145	55	15	a	a	DET
fcis-25145	55	16	certain	certain	ADJ
fcis-25145	55	17	level	level	NOUN
fcis-25145	55	18	of	of	ADP
fcis-25145	55	19	facial	facial	ADJ
fcis-25145	55	20	expression	expression	NOUN
fcis-25145	55	21	recognition	recognition	NOUN
fcis-25145	55	22	performance	performance	NOUN
fcis-25145	55	23	.	.	PUNCT
fcis-25145	56	1	due	due	ADP
fcis-25145	56	2	to	to	ADP
fcis-25145	56	3	the	the	DET
fcis-25145	56	4	limited	limited	ADJ
fcis-25145	56	5	image	image	NOUN
fcis-25145	56	6	data	datum	NOUN
fcis-25145	56	7	in	in	ADP
fcis-25145	56	8	the	the	DET
fcis-25145	56	9	ck+dataset	ck+dataset	NOUN
fcis-25145	56	10	and	and	CCONJ
fcis-25145	56	11	its	its	PRON
fcis-25145	56	12	proximity	proximity	NOUN
fcis-25145	56	13	to	to	ADP
fcis-25145	56	14	natural	natural	ADJ
fcis-25145	56	15	facial	facial	ADJ
fcis-25145	56	16	expression	expression	NOUN
fcis-25145	56	17	images	image	NOUN
fcis-25145	56	18	in	in	ADP
fcis-25145	56	19	daily	daily	ADJ
fcis-25145	56	20	life	life	NOUN
fcis-25145	56	21	,	,	PUNCT
fcis-25145	56	22	the	the	DET
fcis-25145	56	23	next	next	ADJ
fcis-25145	56	24	step	step	NOUN
fcis-25145	56	25	of	of	ADP
fcis-25145	56	26	the	the	DET
fcis-25145	56	27	experiment	experiment	NOUN
fcis-25145	56	28	will	will	AUX
fcis-25145	56	29	use	use	VERB
fcis-25145	56	30	facial	facial	ADJ
fcis-25145	56	31	expression	expression	NOUN
fcis-25145	56	32	images	image	NOUN
fcis-25145	56	33	from	from	ADP
fcis-25145	56	34	daily	daily	ADJ
fcis-25145	56	35	life	life	NOUN
fcis-25145	56	36	or	or	CCONJ
fcis-25145	56	37	videos	video	NOUN
fcis-25145	56	38	to	to	PART
fcis-25145	56	39	train	train	VERB
fcis-25145	56	40	the	the	DET
fcis-25145	56	41	model	model	NOUN
fcis-25145	56	42	.	.	PUNCT
fcis-25145	57	1	in	in	ADP
fcis-25145	57	2	addition	addition	NOUN
fcis-25145	57	3	,	,	PUNCT
fcis-25145	57	4	channel	channel	NOUN
fcis-25145	57	5	attention	attention	NOUN
fcis-25145	57	6	mechanism	mechanism	NOUN
fcis-25145	57	7	will	will	AUX
fcis-25145	57	8	be	be	AUX
fcis-25145	57	9	used	use	VERB
fcis-25145	57	10	to	to	PART
fcis-25145	57	11	construct	construct	VERB
fcis-25145	57	12	a	a	DET
fcis-25145	57	13	network	network	NOUN
fcis-25145	57	14	and	and	CCONJ
fcis-25145	57	15	data	datum	NOUN
fcis-25145	57	16	augmentation	augmentation	NOUN
fcis-25145	57	17	methods	method	NOUN
fcis-25145	57	18	to	to	PART
fcis-25145	57	19	expand	expand	VERB
fcis-25145	57	20	the	the	DET
fcis-25145	57	21	limited	limited	ADJ
fcis-25145	57	22	number	number	NOUN
fcis-25145	57	23	of	of	ADP
fcis-25145	57	24	images	image	NOUN
fcis-25145	57	25	in	in	ADP
fcis-25145	57	26	the	the	DET
fcis-25145	57	27	dataset	dataset	NOUN
fcis-25145	57	28	.	.	PUNCT
fcis-25145	58	1	this	this	DET
fcis-25145	58	2	approach	approach	NOUN
fcis-25145	58	3	can	can	AUX
fcis-25145	58	4	more	more	ADV
fcis-25145	58	5	fully	fully	ADV
fcis-25145	58	6	train	train	VERB
fcis-25145	58	7	the	the	DET
fcis-25145	58	8	model	model	NOUN
fcis-25145	58	9	and	and	CCONJ
fcis-25145	58	10	prevent	prevent	VERB
fcis-25145	58	11	overfitting	overfitting	NOUN
fcis-25145	58	12	of	of	ADP
fcis-25145	58	13	the	the	DET
fcis-25145	58	14	network	network	NOUN
fcis-25145	58	15	.	.	PUNCT
fcis-25145	59	1	acknowledgments	acknowledgment	NOUN
fcis-25145	59	2	focus	focus	VERB
fcis-25145	59	3	analysis	analysis	NOUN
fcis-25145	59	4	of	of	ADP
fcis-25145	59	5	cloud	cloud	ADJ
fcis-25145	59	6	classroom	classroom	NOUN
fcis-25145	59	7	based	base	VERB
fcis-25145	59	8	on	on	ADP
fcis-25145	59	9	expression	expression	NOUN
fcis-25145	59	10	recognition+gky-2023kyqnk-2	recognition+gky-2023kyqnk-2	PROPN
fcis-25145	59	11	.	.	PUNCT
fcis-25145	60	1	references	reference	NOUN
fcis-25145	60	2	[	[	X
fcis-25145	60	3	1	1	NUM
fcis-25145	60	4	]	]	PUNCT
fcis-25145	60	5	ekman	ekman	NOUN
fcis-25145	60	6	p	p	PROPN
fcis-25145	60	7	,	,	PUNCT
fcis-25145	60	8	friesen	friesen	PROPN
fcis-25145	60	9	w	w	PROPN
fcis-25145	60	10	v.	v.	PROPN
fcis-25145	60	11	constants	constant	NOUN
fcis-25145	60	12	across	across	ADP
fcis-25145	60	13	cultures	culture	NOUN
fcis-25145	60	14	in	in	ADP
fcis-25145	60	15	the	the	DET
fcis-25145	60	16	face	face	NOUN
fcis-25145	60	17	and	and	CCONJ
fcis-25145	60	18	emotion[j	emotion[j	PROPN
fcis-25145	60	19	]	]	PUNCT
fcis-25145	60	20	.	.	PUNCT
fcis-25145	61	1	journal	journal	PROPN
fcis-25145	61	2	of	of	ADP
fcis-25145	61	3	personality	personality	NOUN
fcis-25145	61	4	and	and	CCONJ
fcis-25145	61	5	social	social	ADJ
fcis-25145	61	6	psychology	psychology	NOUN
fcis-25145	61	7	,	,	PUNCT
fcis-25145	61	8	1971	1971	NUM
fcis-25145	61	9	,	,	PUNCT
fcis-25145	61	10	17(2	17(2	NUM
fcis-25145	61	11	):	):	PUNCT
fcis-25145	61	12	124	124	NUM
fcis-25145	61	13	.	.	PUNCT
fcis-25145	62	1	[	[	X
fcis-25145	62	2	2	2	X
fcis-25145	62	3	]	]	PUNCT
fcis-25145	62	4	dang	dang	NOUN
fcis-25145	62	5	xin	xin	PROPN
fcis-25145	62	6	,	,	PUNCT
fcis-25145	62	7	xu	xu	PROPN
fcis-25145	62	8	hua	hua	PROPN
fcis-25145	62	9	.	.	PROPN
fcis-25145	63	1	driving	drive	VERB
fcis-25145	63	2	state	state	NOUN
fcis-25145	63	3	analysis	analysis	NOUN
fcis-25145	63	4	based	base	VERB
fcis-25145	63	5	on	on	ADP
fcis-25145	63	6	facial	facial	ADJ
fcis-25145	63	7	expression	expression	NOUN
fcis-25145	63	8	recognition	recognition	NOUN
fcis-25145	64	1	[	[	X
fcis-25145	64	2	j	j	X
fcis-25145	64	3	]	]	X
fcis-25145	64	4	.	.	PUNCT
fcis-25145	65	1	journal	journal	PROPN
fcis-25145	65	2	of	of	ADP
fcis-25145	65	3	information	information	NOUN
fcis-25145	65	4	recording	recording	NOUN
fcis-25145	65	5	materials	material	NOUN
fcis-25145	65	6	,	,	PUNCT
fcis-25145	65	7	2019,25(3):108	2019,25(3):108	NUM
fcis-25145	65	8	-	-	SYM
fcis-25145	65	9	111,114	111,114	NUM
fcis-25145	65	10	.	.	PUNCT
fcis-25145	66	1	[	[	X
fcis-25145	66	2	3	3	NUM
fcis-25145	66	3	]	]	X
fcis-25145	66	4	li	li	PROPN
fcis-25145	66	5	jing	jing	PROPN
fcis-25145	66	6	,	,	PUNCT
fcis-25145	66	7	li	li	PROPN
fcis-25145	66	8	jian	jian	PROPN
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fcis-25145	66	13	et	et	PROPN
fcis-25145	66	14	al	al	PROPN
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fcis-25145	67	1	facial	facial	ADJ
fcis-25145	67	2	expression	expression	NOUN
fcis-25145	67	3	recognition	recognition	NOUN
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fcis-25145	67	5	on	on	ADP
fcis-25145	67	6	occlusion	occlusion	NOUN
fcis-25145	67	7	and	and	CCONJ
fcis-25145	67	8	reconstruction	reconstruction	NOUN
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fcis-25145	67	10	key	key	ADJ
fcis-25145	67	11	areas	area	NOUN
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fcis-25145	68	3	]	]	X
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fcis-25145	69	2	engineering	engineering	NOUN
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fcis-25145	69	5	-	-	SYM
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fcis-25145	69	7	.	.	PUNCT
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fcis-25145	70	6	,	,	PUNCT
fcis-25145	70	7	lu	lu	PROPN
fcis-25145	70	8	yu	yu	PROPN
fcis-25145	70	9	.	.	PUNCT
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fcis-25145	70	11	expression	expression	NOUN
fcis-25145	70	12	recognition	recognition	NOUN
fcis-25145	70	13	based	base	VERB
fcis-25145	70	14	on	on	ADP
fcis-25145	70	15	depth	depth	NOUN
fcis-25145	70	16	-	-	PUNCT
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fcis-25145	70	21	]	]	X
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fcis-25145	71	2	engineering	engineering	NOUN
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fcis-25145	71	5	,	,	PUNCT
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fcis-25145	71	7	-	-	SYM
fcis-25145	71	8	1454	1454	NUM
fcis-25145	71	9	.	.	PUNCT
fcis-25145	72	1	[	[	X
fcis-25145	72	2	5	5	X
fcis-25145	72	3	]	]	X
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fcis-25145	72	5	jin	jin	PROPN
fcis-25145	72	6	,	,	PUNCT
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fcis-25145	72	9	,	,	PUNCT
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fcis-25145	72	12	.	.	PUNCT
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fcis-25145	73	2	facial	facial	ADJ
fcis-25145	73	3	expression	expression	NOUN
fcis-25145	73	4	recognition	recognition	NOUN
fcis-25145	73	5	using	use	VERB
fcis-25145	73	6	spatial	spatial	ADJ
fcis-25145	73	7	grouping	grouping	NOUN
fcis-25145	73	8	to	to	PART
fcis-25145	73	9	enhance	enhance	VERB
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fcis-25145	74	1	[	[	X
fcis-25145	74	2	j	j	X
fcis-25145	74	3	]	]	X
fcis-25145	74	4	.	.	PUNCT
fcis-25145	75	1	computer	computer	NOUN
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fcis-25145	75	5	,	,	PUNCT
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fcis-25145	75	7	,	,	PUNCT
fcis-25145	75	8	59	59	NUM
fcis-25145	75	9	(	(	PUNCT
fcis-25145	75	10	22):233	22):233	NUM
fcis-25145	75	11	-	-	SYM
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fcis-25145	75	13	.	.	PUNCT
fcis-25145	76	1	[	[	X
fcis-25145	76	2	6	6	NUM
fcis-25145	76	3	]	]	PUNCT
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fcis-25145	76	6	xception	xception	PROPN
fcis-25145	76	7	:	:	PUNCT
fcis-25145	76	8	deep	deep	ADJ
fcis-25145	76	9	learning	learn	VERB
fcis-25145	76	10	with	with	ADP
fcis-25145	76	11	depthwise	depthwise	PROPN
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fcis-25145	76	14	]	]	PUNCT
fcis-25145	76	15	.	.	PUNCT
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fcis-25145	77	4	ieee	ieee	NOUN
fcis-25145	77	5	conference	conference	NOUN
fcis-25145	77	6	on	on	ADP
fcis-25145	77	7	computer	computer	NOUN
fcis-25145	77	8	vision	vision	NOUN
fcis-25145	77	9	and	and	CCONJ
fcis-25145	77	10	pattern	pattern	NOUN
fcis-25145	77	11	recognition	recognition	NOUN
fcis-25145	77	12	.	.	PUNCT
fcis-25145	78	1	2017	2017	NUM
fcis-25145	78	2	:	:	PUNCT
fcis-25145	78	3	1251	1251	NUM
fcis-25145	78	4	-	-	SYM
fcis-25145	78	5	1258	1258	NUM
fcis-25145	78	6	.	.	PUNCT
fcis-25145	79	1	[	[	X
fcis-25145	79	2	7	7	X
fcis-25145	79	3	]	]	X
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fcis-25145	79	6	,	,	PUNCT
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fcis-25145	79	9	,	,	PUNCT
fcis-25145	79	10	yao	yao	PROPN
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fcis-25145	79	12	,	,	PUNCT
fcis-25145	79	13	et	et	PROPN
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fcis-25145	79	15	.	.	PUNCT
fcis-25145	80	1	a	a	DET
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fcis-25145	80	4	-	-	PUNCT
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fcis-25145	80	6	network	network	NOUN
fcis-25145	80	7	with	with	ADP
fcis-25145	80	8	discriminant	discriminant	NOUN
fcis-25145	80	9	weighting	weight	VERB
fcis-25145	80	10	loss	loss	NOUN
fcis-25145	80	11	for	for	ADP
fcis-25145	80	12	handwritten	handwritten	ADJ
fcis-25145	80	13	chinese	chinese	ADJ
fcis-25145	80	14	character	character	NOUN
fcis-25145	80	15	recognition[c	recognition[c	PROPN
fcis-25145	80	16	]	]	PUNCT
fcis-25145	80	17	.	.	PUNCT
fcis-25145	81	1	2019	2019	NUM
fcis-25145	81	2	international	international	ADJ
fcis-25145	81	3	conference	conference	NOUN
fcis-25145	81	4	on	on	ADP
fcis-25145	81	5	document	document	NOUN
fcis-25145	81	6	analysis	analysis	NOUN
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fcis-25145	81	8	recognition	recognition	NOUN
fcis-25145	81	9	(	(	PUNCT
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fcis-25145	81	11	)	)	PUNCT
fcis-25145	81	12	.	.	PUNCT
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fcis-25145	82	2	,	,	PUNCT
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fcis-25145	82	4	:	:	PUNCT
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fcis-25145	82	6	-	-	SYM
fcis-25145	82	7	1225	1225	NUM
fcis-25145	82	8	.	.	PUNCT
fcis-25145	83	1	[	[	X
fcis-25145	83	2	8	8	NUM
fcis-25145	83	3	]	]	SYM
fcis-25145	83	4	opschoor	opschoor	ADJ
fcis-25145	83	5	j	j	PROPN
fcis-25145	83	6	a	a	DET
fcis-25145	83	7	a	a	X
fcis-25145	83	8	,	,	PUNCT
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fcis-25145	83	10	p	p	PROPN
fcis-25145	83	11	c	c	PROPN
fcis-25145	83	12	,	,	PUNCT
fcis-25145	83	13	schwab	schwab	PROPN
fcis-25145	83	14	c.	c.	PROPN
fcis-25145	83	15	deep	deep	PROPN
fcis-25145	83	16	relu	relu	NOUN
fcis-25145	83	17	networks	network	NOUN
fcis-25145	83	18	and	and	CCONJ
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fcis-25145	83	20	-	-	PUNCT
fcis-25145	83	21	order	order	NOUN
fcis-25145	83	22	finite	finite	PROPN
fcis-25145	83	23	element	element	NOUN
fcis-25145	83	24	methods[j	methods[j	PROPN
fcis-25145	83	25	]	]	PUNCT
fcis-25145	83	26	.	.	PUNCT
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fcis-25145	84	2	and	and	CCONJ
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fcis-25145	84	4	,	,	PUNCT
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fcis-25145	84	6	,	,	PUNCT
fcis-25145	84	7	18(05	18(05	NUM
fcis-25145	84	8	):	):	PUNCT
fcis-25145	84	9	715	715	NUM
fcis-25145	84	10	-	-	SYM
fcis-25145	84	11	770	770	NUM
fcis-25145	84	12	.	.	PUNCT
fcis-25145	85	1	[	[	X
fcis-25145	85	2	9	9	NUM
fcis-25145	85	3	]	]	X
fcis-25145	85	4	sandler	sandler	NOUN
fcis-25145	85	5	m	m	PROPN
fcis-25145	85	6	,	,	PUNCT
fcis-25145	85	7	howard	howard	PROPN
fcis-25145	85	8	a	a	PRON
fcis-25145	85	9	,	,	PUNCT
fcis-25145	85	10	zhu	zhu	PROPN
fcis-25145	85	11	m	m	PROPN
fcis-25145	85	12	,	,	PUNCT
fcis-25145	85	13	et	et	PROPN
fcis-25145	85	14	al	al	PROPN
fcis-25145	85	15	.	.	PUNCT
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fcis-25145	86	2	:	:	PUNCT
fcis-25145	86	3	inverted	inverted	ADJ
fcis-25145	86	4	residuals	residual	NOUN
fcis-25145	86	5	and	and	CCONJ
fcis-25145	86	6	linear	linear	PROPN
fcis-25145	86	7	bottlenecks[c	bottlenecks[c	PROPN
fcis-25145	86	8	]	]	PUNCT
fcis-25145	86	9	.	.	PUNCT
fcis-25145	87	1	proceedings	proceeding	NOUN
fcis-25145	87	2	of	of	ADP
fcis-25145	87	3	the	the	DET
fcis-25145	87	4	ieee	ieee	NOUN
fcis-25145	87	5	conference	conference	NOUN
fcis-25145	87	6	on	on	ADP
fcis-25145	87	7	computer	computer	NOUN
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fcis-25145	87	9	and	and	CCONJ
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fcis-25145	87	11	recognition	recognition	NOUN
fcis-25145	87	12	.	.	PUNCT
fcis-25145	88	1	2018	2018	NUM
fcis-25145	88	2	:	:	PUNCT
fcis-25145	88	3	4510	4510	NUM
fcis-25145	88	4	-	-	SYM
fcis-25145	88	5	4520	4520	NUM
fcis-25145	88	6	.	.	PUNCT