id	sid	tid	token	lemma	pos
fcis-985	1	1	frontiers	frontier	NOUN
fcis-985	1	2	in	in	ADP
fcis-985	1	3	computing	computing	NOUN
fcis-985	1	4	and	and	CCONJ
fcis-985	1	5	intelligent	intelligent	ADJ
fcis-985	1	6	systems	system	NOUN
fcis-985	1	7	issn	issn	VERB
fcis-985	1	8	:	:	PUNCT
fcis-985	1	9	2832	2832	NUM
fcis-985	1	10	-	-	SYM
fcis-985	1	11	6024	6024	NUM
fcis-985	1	12	|	|	NOUN
fcis-985	1	13	vol	vol	NOUN
fcis-985	1	14	.	.	PROPN
fcis-985	2	1	1	1	NUM
fcis-985	2	2	,	,	PUNCT
fcis-985	2	3	no	no	INTJ
fcis-985	2	4	.	.	NOUN
fcis-985	2	5	1	1	NUM
fcis-985	2	6	,	,	PUNCT
fcis-985	2	7	2022	2022	NUM
fcis-985	2	8	1	1	NUM
fcis-985	2	9	face	face	NOUN
fcis-985	2	10	and	and	CCONJ
fcis-985	2	11	gender	gender	NOUN
fcis-985	2	12	detection	detection	NOUN
fcis-985	2	13	based	base	VERB
fcis-985	2	14	on	on	ADP
fcis-985	2	15	bp	bp	PROPN
fcis-985	2	16	neural	neural	ADJ
fcis-985	2	17	network	network	PROPN
fcis-985	2	18	algorithm	algorithm	PROPN
fcis-985	2	19	ji	ji	PROPN
fcis-985	2	20	zhang	zhang	PROPN
fcis-985	2	21	1	1	NUM
fcis-985	2	22	,	,	PUNCT
fcis-985	2	23	2	2	NUM
fcis-985	2	24	,	,	PUNCT
fcis-985	2	25	zijian	zijian	NOUN
fcis-985	2	26	ran	run	VERB
fcis-985	2	27	1	1	NUM
fcis-985	2	28	,	,	PUNCT
fcis-985	2	29	*	*	PUNCT
fcis-985	2	30	1	1	NUM
fcis-985	2	31	school	school	NOUN
fcis-985	2	32	of	of	ADP
fcis-985	2	33	control	control	NOUN
fcis-985	2	34	and	and	CCONJ
fcis-985	2	35	computer	computer	NOUN
fcis-985	2	36	engineering	engineering	NOUN
fcis-985	2	37	,	,	PUNCT
fcis-985	2	38	north	north	PROPN
fcis-985	2	39	china	china	PROPN
fcis-985	2	40	electric	electric	PROPN
fcis-985	2	41	power	power	PROPN
fcis-985	2	42	university	university	PROPN
fcis-985	2	43	,	,	PUNCT
fcis-985	2	44	baoding	baoding	PROPN
fcis-985	2	45	071003	071003	NUM
fcis-985	2	46	,	,	PUNCT
fcis-985	2	47	hebei	hebei	PROPN
fcis-985	2	48	province	province	PROPN
fcis-985	2	49	,	,	PUNCT
fcis-985	2	50	china	china	PROPN
fcis-985	2	51	2	2	NUM
fcis-985	2	52	engineering	engineering	NOUN
fcis-985	2	53	research	research	NOUN
fcis-985	2	54	center	center	NOUN
fcis-985	2	55	of	of	ADP
fcis-985	2	56	intelligent	intelligent	ADJ
fcis-985	2	57	computing	computing	NOUN
fcis-985	2	58	for	for	ADP
fcis-985	2	59	complex	complex	ADJ
fcis-985	2	60	energy	energy	NOUN
fcis-985	2	61	systems	system	NOUN
fcis-985	2	62	,	,	PUNCT
fcis-985	2	63	ministry	ministry	NOUN
fcis-985	2	64	of	of	ADP
fcis-985	2	65	education	education	PROPN
fcis-985	2	66	,	,	PUNCT
fcis-985	2	67	baoding	baoding	PROPN
fcis-985	2	68	071003	071003	NUM
fcis-985	2	69	,	,	PUNCT
fcis-985	2	70	hebei	hebei	PROPN
fcis-985	2	71	province	province	PROPN
fcis-985	2	72	,	,	PUNCT
fcis-985	2	73	china	china	PROPN
fcis-985	2	74	*	*	PUNCT
fcis-985	2	75	corresponding	correspond	VERB
fcis-985	2	76	author	author	NOUN
fcis-985	2	77	:	:	PUNCT
fcis-985	2	78	zijian	zijian	ADJ
fcis-985	2	79	ran	ran	NOUN
fcis-985	2	80	(	(	PUNCT
fcis-985	2	81	email	email	NOUN
fcis-985	2	82	:	:	PUNCT
fcis-985	2	83	529726064@qq.com	529726064@qq.com	NUM
fcis-985	2	84	)	)	PUNCT
fcis-985	2	85	abstract	abstract	NOUN
fcis-985	2	86	:	:	PUNCT
fcis-985	2	87	face	face	NOUN
fcis-985	2	88	recognition	recognition	NOUN
fcis-985	2	89	technology	technology	NOUN
fcis-985	2	90	has	have	VERB
fcis-985	2	91	a	a	DET
fcis-985	2	92	wide	wide	ADJ
fcis-985	2	93	range	range	NOUN
fcis-985	2	94	of	of	ADP
fcis-985	2	95	applications	application	NOUN
fcis-985	2	96	in	in	ADP
fcis-985	2	97	real	real	ADJ
fcis-985	2	98	life	life	NOUN
fcis-985	2	99	,	,	PUNCT
fcis-985	2	100	and	and	CCONJ
fcis-985	2	101	many	many	ADJ
fcis-985	2	102	applications	application	NOUN
fcis-985	2	103	can	can	AUX
fcis-985	2	104	be	be	AUX
fcis-985	2	105	developed	develop	VERB
fcis-985	2	106	on	on	ADP
fcis-985	2	107	the	the	DET
fcis-985	2	108	basis	basis	NOUN
fcis-985	2	109	of	of	ADP
fcis-985	2	110	face	face	NOUN
fcis-985	2	111	recognition	recognition	NOUN
fcis-985	2	112	,	,	PUNCT
fcis-985	2	113	such	such	ADJ
fcis-985	2	114	as	as	ADP
fcis-985	2	115	gender	gender	NOUN
fcis-985	2	116	recognition	recognition	NOUN
fcis-985	2	117	,	,	PUNCT
fcis-985	2	118	age	age	NOUN
fcis-985	2	119	recognition	recognition	NOUN
fcis-985	2	120	,	,	PUNCT
fcis-985	2	121	face	face	VERB
fcis-985	2	122	comparison	comparison	NOUN
fcis-985	2	123	,	,	PUNCT
fcis-985	2	124	and	and	CCONJ
fcis-985	2	125	beauty	beauty	NOUN
fcis-985	2	126	image	image	NOUN
fcis-985	2	127	decoration	decoration	NOUN
fcis-985	2	128	.	.	PUNCT
fcis-985	3	1	in	in	ADP
fcis-985	3	2	this	this	DET
fcis-985	3	3	paper	paper	NOUN
fcis-985	3	4	,	,	PUNCT
fcis-985	3	5	based	base	VERB
fcis-985	3	6	on	on	ADP
fcis-985	3	7	the	the	DET
fcis-985	3	8	computer	computer	NOUN
fcis-985	3	9	vision	vision	PROPN
fcis-985	3	10	library	library	PROPN
fcis-985	3	11	opencv	opencv	PROPN
fcis-985	3	12	in	in	ADP
fcis-985	3	13	the	the	DET
fcis-985	3	14	python	python	NOUN
fcis-985	3	15	language	language	NOUN
fcis-985	3	16	,	,	PUNCT
fcis-985	3	17	the	the	DET
fcis-985	3	18	face	face	NOUN
fcis-985	3	19	is	be	AUX
fcis-985	3	20	recognized	recognize	VERB
fcis-985	3	21	,	,	PUNCT
fcis-985	3	22	and	and	CCONJ
fcis-985	3	23	the	the	DET
fcis-985	3	24	mature	mature	ADJ
fcis-985	3	25	bp	bp	PROPN
fcis-985	3	26	neural	neural	ADJ
fcis-985	3	27	network	network	NOUN
fcis-985	3	28	algorithm	algorithm	NOUN
fcis-985	3	29	is	be	AUX
fcis-985	3	30	used	use	VERB
fcis-985	3	31	to	to	PART
fcis-985	3	32	learn	learn	VERB
fcis-985	3	33	the	the	DET
fcis-985	3	34	photos	photo	NOUN
fcis-985	3	35	in	in	ADP
fcis-985	3	36	the	the	DET
fcis-985	3	37	database	database	NOUN
fcis-985	3	38	,	,	PUNCT
fcis-985	3	39	and	and	CCONJ
fcis-985	3	40	finally	finally	ADV
fcis-985	3	41	the	the	DET
fcis-985	3	42	gender	gender	NOUN
fcis-985	3	43	detection	detection	NOUN
fcis-985	3	44	of	of	ADP
fcis-985	3	45	the	the	DET
fcis-985	3	46	face	face	NOUN
fcis-985	3	47	is	be	AUX
fcis-985	3	48	successfully	successfully	ADV
fcis-985	3	49	realized	realize	VERB
fcis-985	3	50	.	.	PUNCT
fcis-985	4	1	keywords	keyword	NOUN
fcis-985	4	2	:	:	PUNCT
fcis-985	4	3	face	face	NOUN
fcis-985	4	4	detection	detection	NOUN
fcis-985	4	5	;	;	PUNCT
fcis-985	4	6	gender	gender	NOUN
fcis-985	4	7	detection	detection	NOUN
fcis-985	4	8	;	;	PUNCT
fcis-985	4	9	opencv	opencv	PROPN
fcis-985	4	10	;	;	PUNCT
fcis-985	4	11	bp	bp	PROPN
fcis-985	4	12	neural	neural	ADJ
fcis-985	4	13	network	network	NOUN
fcis-985	4	14	;	;	PUNCT
fcis-985	4	15	python	python	PROPN
fcis-985	4	16	.	.	PUNCT
fcis-985	5	1	1	1	X
fcis-985	5	2	.	.	X
fcis-985	5	3	introduction	introduction	NOUN
fcis-985	5	4	in	in	ADP
fcis-985	5	5	the	the	DET
fcis-985	5	6	rapidly	rapidly	ADV
fcis-985	5	7	developing	develop	VERB
fcis-985	5	8	modern	modern	ADJ
fcis-985	5	9	society	society	NOUN
fcis-985	5	10	,	,	PUNCT
fcis-985	5	11	fast	fast	ADJ
fcis-985	5	12	,	,	PUNCT
fcis-985	5	13	convenient	convenient	ADJ
fcis-985	5	14	and	and	CCONJ
fcis-985	5	15	safe	safe	ADJ
fcis-985	5	16	have	have	AUX
fcis-985	5	17	become	become	VERB
fcis-985	5	18	synonymous	synonymous	ADJ
fcis-985	5	19	with	with	ADP
fcis-985	5	20	contemporary	contemporary	ADJ
fcis-985	5	21	society	society	NOUN
fcis-985	5	22	.	.	PUNCT
fcis-985	6	1	in	in	ADP
fcis-985	6	2	order	order	NOUN
fcis-985	6	3	to	to	PART
fcis-985	6	4	make	make	VERB
fcis-985	6	5	life	life	NOUN
fcis-985	6	6	more	more	ADV
fcis-985	6	7	convenient	convenient	ADJ
fcis-985	6	8	and	and	CCONJ
fcis-985	6	9	save	save	VERB
fcis-985	6	10	people	people	NOUN
fcis-985	6	11	from	from	ADP
fcis-985	6	12	heavy	heavy	ADJ
fcis-985	6	13	labor	labor	NOUN
fcis-985	6	14	,	,	PUNCT
fcis-985	6	15	computer	computer	NOUN
fcis-985	6	16	technology	technology	NOUN
fcis-985	6	17	and	and	CCONJ
fcis-985	6	18	its	its	PRON
fcis-985	6	19	applications	application	NOUN
fcis-985	6	20	have	have	AUX
fcis-985	6	21	become	become	VERB
fcis-985	6	22	an	an	DET
fcis-985	6	23	indispensable	indispensable	ADJ
fcis-985	6	24	role	role	NOUN
fcis-985	6	25	in	in	ADP
fcis-985	6	26	people	people	NOUN
fcis-985	6	27	's	's	PART
fcis-985	6	28	lives	life	NOUN
fcis-985	6	29	.	.	PUNCT
fcis-985	7	1	among	among	ADP
fcis-985	7	2	them	they	PRON
fcis-985	7	3	,	,	PUNCT
fcis-985	7	4	the	the	DET
fcis-985	7	5	development	development	NOUN
fcis-985	7	6	and	and	CCONJ
fcis-985	7	7	application	application	NOUN
fcis-985	7	8	of	of	ADP
fcis-985	7	9	artificial	artificial	ADJ
fcis-985	7	10	intelligence	intelligence	NOUN
fcis-985	7	11	technology	technology	NOUN
fcis-985	7	12	has	have	AUX
fcis-985	7	13	become	become	VERB
fcis-985	7	14	the	the	DET
fcis-985	7	15	focus	focus	NOUN
fcis-985	7	16	and	and	CCONJ
fcis-985	7	17	hotspot	hotspot	NOUN
fcis-985	7	18	of	of	ADP
fcis-985	7	19	domestic	domestic	ADJ
fcis-985	7	20	and	and	CCONJ
fcis-985	7	21	foreign	foreign	ADJ
fcis-985	7	22	scholars	scholar	NOUN
fcis-985	7	23	and	and	CCONJ
fcis-985	7	24	researchers	researcher	NOUN
fcis-985	7	25	.	.	PUNCT
fcis-985	8	1	in	in	ADP
fcis-985	8	2	the	the	DET
fcis-985	8	3	field	field	NOUN
fcis-985	8	4	of	of	ADP
fcis-985	8	5	artificial	artificial	ADJ
fcis-985	8	6	intelligence	intelligence	NOUN
fcis-985	8	7	,	,	PUNCT
fcis-985	8	8	the	the	DET
fcis-985	8	9	research	research	NOUN
fcis-985	8	10	of	of	ADP
fcis-985	8	11	3d	3d	NUM
fcis-985	8	12	face	face	NOUN
fcis-985	8	13	recognition	recognition	NOUN
fcis-985	8	14	technology	technology	NOUN
fcis-985	8	15	has	have	AUX
fcis-985	8	16	become	become	VERB
fcis-985	8	17	a	a	DET
fcis-985	8	18	hot	hot	ADJ
fcis-985	8	19	research	research	NOUN
fcis-985	8	20	field	field	NOUN
fcis-985	8	21	,	,	PUNCT
fcis-985	8	22	and	and	CCONJ
fcis-985	8	23	it	it	PRON
fcis-985	8	24	can	can	AUX
fcis-985	8	25	be	be	AUX
fcis-985	8	26	applied	apply	VERB
fcis-985	8	27	to	to	ADP
fcis-985	8	28	various	various	ADJ
fcis-985	8	29	places	place	NOUN
fcis-985	8	30	that	that	PRON
fcis-985	8	31	require	require	VERB
fcis-985	8	32	identity	identity	NOUN
fcis-985	8	33	authentication	authentication	NOUN
fcis-985	8	34	,	,	PUNCT
fcis-985	8	35	such	such	ADJ
fcis-985	8	36	as	as	ADP
fcis-985	8	37	bank	bank	NOUN
fcis-985	8	38	transaction	transaction	NOUN
fcis-985	8	39	authentication	authentication	NOUN
fcis-985	8	40	in	in	ADP
fcis-985	8	41	the	the	DET
fcis-985	8	42	financial	financial	ADJ
fcis-985	8	43	field	field	NOUN
fcis-985	8	44	,	,	PUNCT
fcis-985	8	45	internet	internet	NOUN
fcis-985	8	46	or	or	CCONJ
fcis-985	8	47	transaction	transaction	NOUN
fcis-985	8	48	identity	identity	NOUN
fcis-985	8	49	verification	verification	NOUN
fcis-985	8	50	on	on	ADP
fcis-985	8	51	online	online	ADJ
fcis-985	8	52	banking	banking	NOUN
fcis-985	8	53	,	,	PUNCT
fcis-985	8	54	access	access	NOUN
fcis-985	8	55	security	security	NOUN
fcis-985	8	56	check	check	NOUN
fcis-985	8	57	in	in	ADP
fcis-985	8	58	national	national	ADJ
fcis-985	8	59	security	security	NOUN
fcis-985	8	60	defense	defense	NOUN
fcis-985	8	61	,	,	PUNCT
fcis-985	8	62	various	various	ADJ
fcis-985	8	63	monitoring	monitoring	NOUN
fcis-985	8	64	systems	system	NOUN
fcis-985	8	65	and	and	CCONJ
fcis-985	8	66	access	access	NOUN
fcis-985	8	67	control	control	NOUN
fcis-985	8	68	systems	system	NOUN
fcis-985	8	69	are	be	AUX
fcis-985	8	70	effective	effective	ADJ
fcis-985	8	71	means	mean	NOUN
fcis-985	8	72	of	of	ADP
fcis-985	8	73	identity	identity	NOUN
fcis-985	8	74	recognition	recognition	NOUN
fcis-985	9	1	[	[	X
fcis-985	9	2	1	1	NUM
fcis-985	9	3	-	-	SYM
fcis-985	9	4	3	3	NUM
fcis-985	9	5	]	]	PUNCT
fcis-985	9	6	.	.	PUNCT
fcis-985	10	1	as	as	ADV
fcis-985	10	2	early	early	ADV
fcis-985	10	3	as	as	ADP
fcis-985	10	4	1964	1964	NUM
fcis-985	10	5	,	,	PUNCT
fcis-985	10	6	foreign	foreign	ADJ
fcis-985	10	7	personnel	personnel	NOUN
fcis-985	10	8	began	begin	VERB
fcis-985	10	9	to	to	PART
fcis-985	10	10	engage	engage	VERB
fcis-985	10	11	in	in	ADP
fcis-985	10	12	the	the	DET
fcis-985	10	13	research	research	NOUN
fcis-985	10	14	of	of	ADP
fcis-985	10	15	face	face	NOUN
fcis-985	10	16	recognition	recognition	NOUN
fcis-985	10	17	technology	technology	NOUN
fcis-985	10	18	.	.	PUNCT
fcis-985	11	1	face	face	NOUN
fcis-985	11	2	recognition	recognition	NOUN
fcis-985	11	3	is	be	AUX
fcis-985	11	4	a	a	DET
fcis-985	11	5	form	form	NOUN
fcis-985	11	6	of	of	ADP
fcis-985	11	7	biometric	biometric	ADJ
fcis-985	11	8	technology	technology	NOUN
fcis-985	11	9	,	,	PUNCT
fcis-985	11	10	which	which	PRON
fcis-985	11	11	involves	involve	VERB
fcis-985	11	12	many	many	ADJ
fcis-985	11	13	methods	method	NOUN
fcis-985	11	14	in	in	ADP
fcis-985	11	15	pattern	pattern	NOUN
fcis-985	11	16	recognition	recognition	NOUN
fcis-985	11	17	,	,	PUNCT
fcis-985	11	18	computer	computer	NOUN
fcis-985	11	19	vision	vision	NOUN
fcis-985	11	20	,	,	PUNCT
fcis-985	11	21	psychology	psychology	NOUN
fcis-985	11	22	,	,	PUNCT
fcis-985	11	23	physiology	physiology	NOUN
fcis-985	11	24	,	,	PUNCT
fcis-985	11	25	and	and	CCONJ
fcis-985	11	26	cognitive	cognitive	ADJ
fcis-985	11	27	science	science	NOUN
fcis-985	11	28	.	.	PUNCT
fcis-985	12	1	the	the	DET
fcis-985	12	2	realization	realization	NOUN
fcis-985	12	3	of	of	ADP
fcis-985	12	4	identity	identity	NOUN
fcis-985	12	5	recognition	recognition	NOUN
fcis-985	12	6	with	with	ADP
fcis-985	12	7	the	the	DET
fcis-985	12	8	aid	aid	NOUN
fcis-985	12	9	of	of	ADP
fcis-985	12	10	computers	computer	NOUN
fcis-985	12	11	is	be	AUX
fcis-985	12	12	based	base	VERB
fcis-985	12	13	on	on	ADP
fcis-985	12	14	the	the	DET
fcis-985	12	15	unique	unique	ADJ
fcis-985	12	16	characteristics	characteristic	NOUN
fcis-985	12	17	of	of	ADP
fcis-985	12	18	people	people	NOUN
fcis-985	12	19	.	.	PUNCT
fcis-985	13	1	an	an	DET
fcis-985	13	2	efficient	efficient	ADJ
fcis-985	13	3	means	mean	NOUN
fcis-985	13	4	of	of	ADP
fcis-985	13	5	authentication	authentication	NOUN
fcis-985	13	6	.	.	PUNCT
fcis-985	14	1	it	it	PRON
fcis-985	14	2	can	can	AUX
fcis-985	14	3	also	also	ADV
fcis-985	14	4	be	be	AUX
fcis-985	14	5	said	say	VERB
fcis-985	14	6	that	that	SCONJ
fcis-985	14	7	face	face	NOUN
fcis-985	14	8	recognition	recognition	NOUN
fcis-985	14	9	also	also	ADV
fcis-985	14	10	refers	refer	VERB
fcis-985	14	11	to	to	ADP
fcis-985	14	12	a	a	DET
fcis-985	14	13	given	give	VERB
fcis-985	14	14	face	face	NOUN
fcis-985	14	15	as	as	ADP
fcis-985	14	16	an	an	DET
fcis-985	14	17	input	input	NOUN
fcis-985	14	18	,	,	PUNCT
fcis-985	14	19	looking	look	VERB
fcis-985	14	20	for	for	ADP
fcis-985	14	21	a	a	DET
fcis-985	14	22	match	match	NOUN
fcis-985	14	23	in	in	ADP
fcis-985	14	24	the	the	DET
fcis-985	14	25	database	database	NOUN
fcis-985	14	26	to	to	PART
fcis-985	14	27	be	be	AUX
fcis-985	14	28	recognized	recognize	VERB
fcis-985	14	29	,	,	PUNCT
fcis-985	14	30	and	and	CCONJ
fcis-985	14	31	finding	find	VERB
fcis-985	14	32	a	a	DET
fcis-985	14	33	face	face	NOUN
fcis-985	14	34	image	image	NOUN
fcis-985	14	35	consistent	consistent	ADJ
fcis-985	14	36	with	with	ADP
fcis-985	14	37	the	the	DET
fcis-985	14	38	input	input	NOUN
fcis-985	14	39	face	face	NOUN
fcis-985	14	40	in	in	ADP
fcis-985	14	41	the	the	DET
fcis-985	14	42	database	database	NOUN
fcis-985	14	43	.	.	PUNCT
fcis-985	15	1	a	a	DET
fcis-985	15	2	large	large	ADJ
fcis-985	15	3	number	number	NOUN
fcis-985	15	4	of	of	ADP
fcis-985	15	5	researchers	researcher	NOUN
fcis-985	15	6	have	have	AUX
fcis-985	15	7	invested	invest	VERB
fcis-985	15	8	in	in	ADP
fcis-985	15	9	the	the	DET
fcis-985	15	10	research	research	NOUN
fcis-985	15	11	of	of	ADP
fcis-985	15	12	eyelid	eyelid	PROPN
fcis-985	15	13	recognition	recognition	NOUN
fcis-985	15	14	,	,	PUNCT
fcis-985	15	15	which	which	PRON
fcis-985	15	16	has	have	AUX
fcis-985	15	17	made	make	VERB
fcis-985	15	18	the	the	DET
fcis-985	15	19	technology	technology	NOUN
fcis-985	15	20	of	of	ADP
fcis-985	15	21	eyelid	eyelid	NOUN
fcis-985	15	22	recognition	recognition	NOUN
fcis-985	15	23	develop	develop	VERB
fcis-985	15	24	rapidly	rapidly	ADV
fcis-985	15	25	.	.	PUNCT
fcis-985	16	1	although	although	SCONJ
fcis-985	16	2	face	face	NOUN
fcis-985	16	3	recognition	recognition	NOUN
fcis-985	16	4	technology	technology	NOUN
fcis-985	16	5	has	have	AUX
fcis-985	16	6	been	be	AUX
fcis-985	16	7	researched	research	VERB
fcis-985	16	8	for	for	ADP
fcis-985	16	9	a	a	DET
fcis-985	16	10	long	long	ADJ
fcis-985	16	11	time	time	NOUN
fcis-985	16	12	and	and	CCONJ
fcis-985	16	13	has	have	AUX
fcis-985	16	14	been	be	AUX
fcis-985	16	15	developed	develop	VERB
fcis-985	16	16	for	for	ADP
fcis-985	16	17	many	many	ADJ
fcis-985	16	18	years	year	NOUN
fcis-985	16	19	,	,	PUNCT
fcis-985	16	20	it	it	PRON
fcis-985	16	21	still	still	ADV
fcis-985	16	22	fails	fail	VERB
fcis-985	16	23	to	to	PART
fcis-985	16	24	achieve	achieve	VERB
fcis-985	16	25	people	people	NOUN
fcis-985	16	26	's	's	PART
fcis-985	16	27	expectations	expectation	NOUN
fcis-985	16	28	.	.	PUNCT
fcis-985	17	1	therefore	therefore	ADV
fcis-985	17	2	,	,	PUNCT
fcis-985	17	3	people	people	NOUN
fcis-985	17	4	's	's	PART
fcis-985	17	5	research	research	NOUN
fcis-985	17	6	on	on	ADP
fcis-985	17	7	face	face	NOUN
fcis-985	17	8	recognition	recognition	NOUN
fcis-985	17	9	technology	technology	NOUN
fcis-985	17	10	is	be	AUX
fcis-985	17	11	still	still	ADV
fcis-985	17	12	under	under	ADP
fcis-985	17	13	exploration	exploration	NOUN
fcis-985	17	14	[	[	X
fcis-985	17	15	4	4	NUM
fcis-985	17	16	-	-	SYM
fcis-985	17	17	5	5	NUM
fcis-985	17	18	]	]	PUNCT
fcis-985	17	19	.	.	PUNCT
fcis-985	18	1	2	2	X
fcis-985	18	2	.	.	X
fcis-985	18	3	bp	bp	PROPN
fcis-985	18	4	neural	neural	ADJ
fcis-985	18	5	network	network	PROPN
fcis-985	18	6	algorithm	algorithm	PROPN
fcis-985	18	7	2.1	2.1	NUM
fcis-985	18	8	.	.	PUNCT
fcis-985	19	1	introduction	introduction	NOUN
fcis-985	19	2	and	and	CCONJ
fcis-985	19	3	practice	practice	NOUN
fcis-985	19	4	bp	bp	PROPN
fcis-985	19	5	neural	neural	ADJ
fcis-985	19	6	network	network	NOUN
fcis-985	19	7	usually	usually	ADV
fcis-985	19	8	refers	refer	VERB
fcis-985	19	9	to	to	ADP
fcis-985	19	10	a	a	DET
fcis-985	19	11	multi	multi	ADJ
fcis-985	19	12	-	-	ADJ
fcis-985	19	13	layer	layer	ADJ
fcis-985	19	14	forward	forward	ADV
fcis-985	19	15	neural	neural	ADJ
fcis-985	19	16	network	network	NOUN
fcis-985	19	17	based	base	VERB
fcis-985	19	18	on	on	ADP
fcis-985	19	19	the	the	DET
fcis-985	19	20	error	error	NOUN
fcis-985	19	21	back	back	NOUN
fcis-985	19	22	propagation	propagation	NOUN
fcis-985	19	23	algorithm	algorithm	NOUN
fcis-985	19	24	.	.	PUNCT
fcis-985	20	1	the	the	DET
fcis-985	20	2	transfer	transfer	NOUN
fcis-985	20	3	function	function	NOUN
fcis-985	20	4	used	use	VERB
fcis-985	20	5	by	by	ADP
fcis-985	20	6	the	the	DET
fcis-985	20	7	neurons	neuron	NOUN
fcis-985	20	8	of	of	ADP
fcis-985	20	9	the	the	DET
fcis-985	20	10	bp	bp	PROPN
fcis-985	20	11	network	network	NOUN
fcis-985	20	12	is	be	AUX
fcis-985	20	13	usually	usually	ADV
fcis-985	20	14	a	a	DET
fcis-985	20	15	sigmoid	sigmoid	NOUN
fcis-985	20	16	-	-	PUNCT
fcis-985	20	17	type	type	NOUN
fcis-985	20	18	differentiable	differentiable	ADJ
fcis-985	20	19	function	function	NOUN
fcis-985	20	20	,	,	PUNCT
fcis-985	20	21	so	so	CCONJ
fcis-985	20	22	any	any	DET
fcis-985	20	23	nonlinear	nonlinear	ADJ
fcis-985	20	24	mapping	mapping	NOUN
fcis-985	20	25	between	between	ADP
fcis-985	20	26	input	input	NOUN
fcis-985	20	27	and	and	CCONJ
fcis-985	20	28	output	output	NOUN
fcis-985	20	29	can	can	AUX
fcis-985	20	30	be	be	AUX
fcis-985	20	31	realized	realize	VERB
fcis-985	20	32	,	,	PUNCT
fcis-985	20	33	which	which	PRON
fcis-985	20	34	makes	make	VERB
fcis-985	20	35	it	it	PRON
fcis-985	20	36	suitable	suitable	ADJ
fcis-985	20	37	for	for	ADP
fcis-985	20	38	applications	application	NOUN
fcis-985	20	39	such	such	ADJ
fcis-985	20	40	as	as	ADP
fcis-985	20	41	signal	signal	ADJ
fcis-985	20	42	processing	processing	NOUN
fcis-985	20	43	,	,	PUNCT
fcis-985	20	44	computer	computer	NOUN
fcis-985	20	45	networks	network	NOUN
fcis-985	20	46	,	,	PUNCT
fcis-985	20	47	process	process	NOUN
fcis-985	20	48	control	control	NOUN
fcis-985	20	49	,	,	PUNCT
fcis-985	20	50	speech	speech	NOUN
fcis-985	20	51	recognition	recognition	NOUN
fcis-985	20	52	,	,	PUNCT
fcis-985	20	53	function	function	NOUN
fcis-985	20	54	approximation	approximation	NOUN
fcis-985	20	55	,	,	PUNCT
fcis-985	20	56	successful	successful	ADJ
fcis-985	20	57	applications	application	NOUN
fcis-985	20	58	have	have	AUX
fcis-985	20	59	been	be	AUX
fcis-985	20	60	achieved	achieve	VERB
fcis-985	20	61	in	in	ADP
fcis-985	20	62	areas	area	NOUN
fcis-985	20	63	such	such	ADJ
fcis-985	20	64	as	as	ADP
fcis-985	20	65	pattern	pattern	NOUN
fcis-985	20	66	recognition	recognition	NOUN
fcis-985	20	67	and	and	CCONJ
fcis-985	20	68	data	datum	NOUN
fcis-985	20	69	compression	compression	NOUN
fcis-985	20	70	.	.	PUNCT
fcis-985	21	1	2.2	2.2	NUM
fcis-985	21	2	.	.	PUNCT
fcis-985	21	3	bp	bp	PROPN
fcis-985	21	4	neural	neural	ADJ
fcis-985	21	5	network	network	NOUN
fcis-985	21	6	structure	structure	NOUN
fcis-985	21	7	each	each	DET
fcis-985	21	8	neuron	neuron	NOUN
fcis-985	21	9	is	be	AUX
fcis-985	21	10	represented	represent	VERB
fcis-985	21	11	by	by	ADP
fcis-985	21	12	a	a	DET
fcis-985	21	13	node	node	NOUN
fcis-985	21	14	,	,	PUNCT
fcis-985	21	15	and	and	CCONJ
fcis-985	21	16	the	the	DET
fcis-985	21	17	network	network	NOUN
fcis-985	21	18	consists	consist	VERB
fcis-985	21	19	of	of	ADP
fcis-985	21	20	input	input	NOUN
fcis-985	21	21	layer	layer	NOUN
fcis-985	21	22	,	,	PUNCT
fcis-985	21	23	hidden	hide	VERB
fcis-985	21	24	layer	layer	NOUN
fcis-985	21	25	and	and	CCONJ
fcis-985	21	26	output	output	NOUN
fcis-985	21	27	layer	layer	NOUN
fcis-985	21	28	nodes	node	NOUN
fcis-985	21	29	.	.	PUNCT
fcis-985	22	1	the	the	DET
fcis-985	22	2	hidden	hide	VERB
fcis-985	22	3	layer	layer	NOUN
fcis-985	22	4	can	can	AUX
fcis-985	22	5	be	be	AUX
fcis-985	22	6	one	one	NUM
fcis-985	22	7	layer	layer	NOUN
fcis-985	22	8	or	or	CCONJ
fcis-985	22	9	multiple	multiple	ADJ
fcis-985	22	10	layers	layer	NOUN
fcis-985	22	11	,	,	PUNCT
fcis-985	22	12	and	and	CCONJ
fcis-985	22	13	the	the	DET
fcis-985	22	14	nodes	node	NOUN
fcis-985	22	15	from	from	ADP
fcis-985	22	16	the	the	DET
fcis-985	22	17	front	front	ADJ
fcis-985	22	18	layer	layer	NOUN
fcis-985	22	19	to	to	ADP
fcis-985	22	20	the	the	DET
fcis-985	22	21	back	back	ADJ
fcis-985	22	22	layer	layer	NOUN
fcis-985	22	23	are	be	AUX
fcis-985	22	24	connected	connect	VERB
fcis-985	22	25	by	by	ADP
fcis-985	22	26	weights	weight	NOUN
fcis-985	22	27	.	.	PUNCT
fcis-985	23	1	hidden	hide	VERB
fcis-985	23	2	nodes	node	NOUN
fcis-985	23	3	generally	generally	ADV
fcis-985	23	4	use	use	VERB
fcis-985	23	5	sigmoid	sigmoid	NOUN
fcis-985	23	6	functions	function	NOUN
fcis-985	23	7	,	,	PUNCT
fcis-985	23	8	and	and	CCONJ
fcis-985	23	9	input	input	NOUN
fcis-985	23	10	and	and	CCONJ
fcis-985	23	11	output	output	NOUN
fcis-985	23	12	nodes	node	NOUN
fcis-985	23	13	can	can	AUX
fcis-985	23	14	use	use	VERB
fcis-985	23	15	sigmoid	sigmoid	NOUN
fcis-985	23	16	functions	function	NOUN
fcis-985	23	17	or	or	CCONJ
fcis-985	23	18	linear	linear	ADJ
fcis-985	23	19	functions	function	NOUN
fcis-985	23	20	.	.	PUNCT
fcis-985	24	1	because	because	SCONJ
fcis-985	24	2	the	the	DET
fcis-985	24	3	bp	bp	PROPN
fcis-985	24	4	algorithm	algorithm	PROPN
fcis-985	24	5	is	be	AUX
fcis-985	24	6	used	use	VERB
fcis-985	24	7	,	,	PUNCT
fcis-985	24	8	it	it	PRON
fcis-985	24	9	is	be	AUX
fcis-985	24	10	often	often	ADV
fcis-985	24	11	called	call	VERB
fcis-985	24	12	bp	bp	PROPN
fcis-985	24	13	neural	neural	ADJ
fcis-985	24	14	network	network	NOUN
fcis-985	24	15	.	.	PUNCT
fcis-985	25	1	2.3	2.3	NUM
fcis-985	25	2	.	.	PUNCT
fcis-985	26	1	principle	principle	NOUN
fcis-985	26	2	of	of	ADP
fcis-985	26	3	bp	bp	PROPN
fcis-985	26	4	algorithm	algorithm	PROPN
fcis-985	26	5	the	the	DET
fcis-985	26	6	bp	bp	PROPN
fcis-985	26	7	algorithm	algorithm	PROPN
fcis-985	26	8	consists	consist	VERB
fcis-985	26	9	of	of	ADP
fcis-985	26	10	two	two	NUM
fcis-985	26	11	parts	part	NOUN
fcis-985	26	12	:	:	PUNCT
fcis-985	26	13	forward	forward	ADJ
fcis-985	26	14	propagation	propagation	NOUN
fcis-985	26	15	and	and	CCONJ
fcis-985	26	16	back	back	ADJ
fcis-985	26	17	propagation	propagation	NOUN
fcis-985	26	18	.	.	PUNCT
fcis-985	27	1	in	in	ADP
fcis-985	27	2	the	the	DET
fcis-985	27	3	process	process	NOUN
fcis-985	27	4	of	of	ADP
fcis-985	27	5	forward	forward	ADJ
fcis-985	27	6	propagation	propagation	NOUN
fcis-985	27	7	,	,	PUNCT
fcis-985	27	8	the	the	DET
fcis-985	27	9	input	input	NOUN
fcis-985	27	10	information	information	NOUN
fcis-985	27	11	is	be	AUX
fcis-985	27	12	transmitted	transmit	VERB
fcis-985	27	13	from	from	ADP
fcis-985	27	14	the	the	DET
fcis-985	27	15	input	input	NOUN
fcis-985	27	16	layer	layer	NOUN
fcis-985	27	17	to	to	ADP
fcis-985	27	18	the	the	DET
fcis-985	27	19	output	output	NOUN
fcis-985	27	20	layer	layer	NOUN
fcis-985	27	21	after	after	ADP
fcis-985	27	22	being	be	AUX
fcis-985	27	23	processed	process	VERB
fcis-985	27	24	by	by	ADP
fcis-985	27	25	the	the	DET
fcis-985	27	26	hidden	hide	VERB
fcis-985	27	27	layer	layer	NOUN
fcis-985	27	28	unit	unit	NOUN
fcis-985	27	29	.	.	PUNCT
fcis-985	28	1	the	the	DET
fcis-985	28	2	state	state	NOUN
fcis-985	28	3	of	of	ADP
fcis-985	28	4	each	each	DET
fcis-985	28	5	layer	layer	NOUN
fcis-985	28	6	of	of	ADP
fcis-985	28	7	neurons	neuron	NOUN
fcis-985	28	8	only	only	ADV
fcis-985	28	9	affects	affect	VERB
fcis-985	28	10	the	the	DET
fcis-985	28	11	state	state	NOUN
fcis-985	28	12	of	of	ADP
fcis-985	28	13	the	the	DET
fcis-985	28	14	next	next	ADJ
fcis-985	28	15	layer	layer	NOUN
fcis-985	28	16	of	of	ADP
fcis-985	28	17	neurons	neuron	NOUN
fcis-985	28	18	.	.	PUNCT
fcis-985	29	1	if	if	SCONJ
fcis-985	29	2	the	the	DET
fcis-985	29	3	output	output	NOUN
fcis-985	29	4	layer	layer	NOUN
fcis-985	29	5	does	do	AUX
fcis-985	29	6	not	not	PART
fcis-985	29	7	get	get	VERB
fcis-985	29	8	the	the	DET
fcis-985	29	9	desired	desire	VERB
fcis-985	29	10	output	output	NOUN
fcis-985	29	11	,	,	PUNCT
fcis-985	29	12	it	it	PRON
fcis-985	29	13	turns	turn	VERB
fcis-985	29	14	to	to	ADP
fcis-985	29	15	backpropagation	backpropagation	NOUN
fcis-985	29	16	,	,	PUNCT
fcis-985	29	17	that	that	ADV
fcis-985	29	18	is	is	ADV
fcis-985	29	19	,	,	PUNCT
fcis-985	29	20	the	the	DET
fcis-985	29	21	error	error	NOUN
fcis-985	29	22	signal	signal	NOUN
fcis-985	29	23	is	be	AUX
fcis-985	29	24	returned	return	VERB
fcis-985	29	25	along	along	ADP
fcis-985	29	26	the	the	DET
fcis-985	29	27	connection	connection	NOUN
fcis-985	29	28	path	path	NOUN
fcis-985	29	29	,	,	PUNCT
fcis-985	29	30	and	and	CCONJ
fcis-985	29	31	the	the	DET
fcis-985	29	32	error	error	NOUN
fcis-985	29	33	signal	signal	NOUN
fcis-985	29	34	is	be	AUX
fcis-985	29	35	minimized	minimize	VERB
fcis-985	29	36	by	by	ADP
fcis-985	29	37	modifying	modify	VERB
fcis-985	29	38	the	the	DET
fcis-985	29	39	connection	connection	NOUN
fcis-985	29	40	weights	weight	NOUN
fcis-985	29	41	between	between	ADP
fcis-985	29	42	neurons	neuron	NOUN
fcis-985	29	43	in	in	ADP
fcis-985	29	44	each	each	DET
fcis-985	29	45	layer	layer	NOUN
fcis-985	30	1	[	[	X
fcis-985	30	2	6	6	NUM
fcis-985	30	3	]	]	PUNCT
fcis-985	30	4	.	.	PUNCT
fcis-985	31	1	since	since	SCONJ
fcis-985	31	2	the	the	DET
fcis-985	31	3	multiple	multiple	ADJ
fcis-985	31	4	-	-	PUNCT
fcis-985	31	5	input	input	NOUN
fcis-985	31	6	-	-	PUNCT
fcis-985	31	7	multiple	multiple	ADJ
fcis-985	31	8	-	-	PUNCT
fcis-985	31	9	output	output	NOUN
fcis-985	31	10	network	network	NOUN
fcis-985	31	11	can	can	AUX
fcis-985	31	12	be	be	AUX
fcis-985	31	13	transformed	transform	VERB
fcis-985	31	14	into	into	ADP
fcis-985	31	15	a	a	DET
fcis-985	31	16	multiple	multiple	ADJ
fcis-985	31	17	-	-	PUNCT
fcis-985	31	18	input	input	NOUN
fcis-985	31	19	-	-	PUNCT
fcis-985	31	20	single	single	ADJ
fcis-985	31	21	-	-	PUNCT
fcis-985	31	22	output	output	NOUN
fcis-985	31	23	situation	situation	NOUN
fcis-985	31	24	,	,	PUNCT
fcis-985	31	25	a	a	DET
fcis-985	31	26	multiple	multiple	ADJ
fcis-985	31	27	-	-	PUNCT
fcis-985	31	28	input	input	NOUN
fcis-985	31	29	-	-	PUNCT
fcis-985	31	30	single	single	ADJ
fcis-985	31	31	-	-	PUNCT
fcis-985	31	32	output	output	NOUN
fcis-985	31	33	neural	neural	ADJ
fcis-985	31	34	model	model	NOUN
fcis-985	31	35	is	be	AUX
fcis-985	31	36	used	use	VERB
fcis-985	31	37	here	here	ADV
fcis-985	31	38	.	.	PUNCT
fcis-985	32	1	suppose	suppose	VERB
fcis-985	32	2	the	the	DET
fcis-985	32	3	network	network	NOUN
fcis-985	32	4	consists	consist	VERB
fcis-985	32	5	of	of	ADP
fcis-985	32	6	m	m	NOUN
fcis-985	32	7	layers	layer	NOUN
fcis-985	32	8	,	,	PUNCT
fcis-985	32	9	and	and	CCONJ
fcis-985	32	10	the	the	DET
fcis-985	32	11	mth	mth	NOUN
fcis-985	32	12	layer	layer	NOUN
fcis-985	32	13	only	only	ADV
fcis-985	32	14	contains	contain	VERB
fcis-985	32	15	output	output	NOUN
fcis-985	32	16	nodes	node	NOUN
fcis-985	32	17	,	,	PUNCT
fcis-985	32	18	and	and	CCONJ
fcis-985	32	19	the	the	DET
fcis-985	32	20	first	first	ADJ
fcis-985	32	21	layer	layer	NOUN
fcis-985	32	22	is	be	AUX
fcis-985	32	23	the	the	DET
fcis-985	32	24	input	input	NOUN
fcis-985	32	25	node	node	NOUN
fcis-985	32	26	.	.	PUNCT
fcis-985	33	1	in	in	ADP
fcis-985	33	2	addition	addition	NOUN
fcis-985	33	3	,	,	PUNCT
fcis-985	33	4	assuming	assume	VERB
fcis-985	33	5	that	that	SCONJ
fcis-985	33	6	there	there	PRON
fcis-985	33	7	are	be	VERB
fcis-985	33	8	n	n	PRON
fcis-985	33	9	standard	standard	ADJ
fcis-985	33	10	sample	sample	NOUN
fcis-985	33	11	pairs	pair	NOUN
fcis-985	33	12	undefined	undefined	ADJ
fcis-985	33	13	,	,	PUNCT
fcis-985	33	14	the	the	DET
fcis-985	33	15	output	output	NOUN
fcis-985	33	16	to	to	ADP
fcis-985	33	17	the	the	DET
fcis-985	33	18	mth	mth	NOUN
fcis-985	33	19	layer	layer	NOUN
fcis-985	33	20	of	of	ADP
fcis-985	33	21	the	the	DET
fcis-985	33	22	network	network	NOUN
fcis-985	33	23	is	be	AUX
fcis-985	33	24	o(p	o(p	PROPN
fcis-985	33	25	)	)	PUNCT
fcis-985	33	26	,	,	PUNCT
fcis-985	33	27	and	and	CCONJ
fcis-985	33	28	the	the	DET
fcis-985	33	29	function	function	NOUN
fcis-985	33	30	function	function	NOUN
fcis-985	33	31	of	of	ADP
fcis-985	33	32	the	the	DET
fcis-985	33	33	hidden	hide	VERB
fcis-985	33	34	layer	layer	NOUN
fcis-985	33	35	node	node	NOUN
fcis-985	33	36	and	and	CCONJ
fcis-985	33	37	the	the	DET
fcis-985	33	38	output	output	NOUN
fcis-985	33	39	layer	layer	NOUN
fcis-985	33	40	node	node	NOUN
fcis-985	33	41	is	be	AUX
fcis-985	33	42	the	the	DET
fcis-985	33	43	sigmoid	sigmoid	NOUN
fcis-985	33	44	line	line	NOUN
fcis-985	33	45	function	function	NOUN
fcis-985	33	46	,	,	PUNCT
fcis-985	33	47	that	that	PRON
fcis-985	33	48	is	be	AUX
fcis-985	33	49	2	2	NUM
fcis-985	33	50	qxe	qxe	NOUN
fcis-985	33	51	xf	xf	PROPN
fcis-985	33	52	−+	−+	PROPN
fcis-985	33	53	=	=	NOUN
fcis-985	33	54	1	1	NUM
fcis-985	33	55	1	1	NUM
fcis-985	33	56	)	)	PUNCT
fcis-985	33	57	(	(	PUNCT
fcis-985	33	58	(	(	PUNCT
fcis-985	33	59	1	1	X
fcis-985	33	60	)	)	PUNCT
fcis-985	33	61	it	it	PRON
fcis-985	33	62	reflects	reflect	VERB
fcis-985	33	63	the	the	DET
fcis-985	33	64	saturation	saturation	NOUN
fcis-985	33	65	property	property	NOUN
fcis-985	33	66	of	of	ADP
fcis-985	33	67	the	the	DET
fcis-985	33	68	neuron	neuron	NOUN
fcis-985	33	69	and	and	CCONJ
fcis-985	33	70	takes	take	VERB
fcis-985	33	71	values	value	NOUN
fcis-985	33	72	between	between	ADP
fcis-985	33	73	0	0	NUM
fcis-985	33	74	and	and	CCONJ
fcis-985	33	75	1	1	NUM
fcis-985	33	76	.	.	PUNCT
fcis-985	34	1	in	in	ADP
fcis-985	34	2	the	the	DET
fcis-985	34	3	above	above	ADJ
fcis-985	34	4	formula	formula	NOUN
fcis-985	34	5	,	,	PUNCT
fcis-985	34	6	q	q	PUNCT
fcis-985	34	7	is	be	AUX
fcis-985	34	8	a	a	DET
fcis-985	34	9	parameter	parameter	NOUN
fcis-985	34	10	representing	represent	VERB
fcis-985	34	11	the	the	DET
fcis-985	34	12	nonlinearity	nonlinearity	NOUN
fcis-985	34	13	of	of	ADP
fcis-985	34	14	the	the	DET
fcis-985	34	15	neuron	neuron	NOUN
fcis-985	34	16	,	,	PUNCT
fcis-985	34	17	which	which	PRON
fcis-985	34	18	is	be	AUX
fcis-985	34	19	called	call	VERB
fcis-985	34	20	a	a	DET
fcis-985	34	21	gain	gain	NOUN
fcis-985	34	22	type	type	NOUN
fcis-985	34	23	(	(	PUNCT
fcis-985	34	24	gain	gain	NOUN
fcis-985	34	25	)	)	PUNCT
fcis-985	34	26	,	,	PUNCT
fcis-985	34	27	also	also	ADV
fcis-985	34	28	called	call	VERB
fcis-985	34	29	an	an	DET
fcis-985	34	30	adjustment	adjustment	NOUN
fcis-985	34	31	parameter	parameter	NOUN
fcis-985	34	32	.	.	PUNCT
fcis-985	35	1	the	the	PRON
fcis-985	35	2	larger	large	ADJ
fcis-985	35	3	the	the	DET
fcis-985	35	4	q	q	NOUN
fcis-985	35	5	value	value	NOUN
fcis-985	35	6	,	,	PUNCT
fcis-985	35	7	the	the	DET
fcis-985	35	8	steeper	steep	ADJ
fcis-985	35	9	the	the	DET
fcis-985	35	10	s	s	NOUN
fcis-985	35	11	-	-	PUNCT
fcis-985	35	12	shaped	shape	VERB
fcis-985	35	13	curve	curve	NOUN
fcis-985	35	14	;	;	PUNCT
fcis-985	35	15	on	on	ADP
fcis-985	35	16	the	the	DET
fcis-985	35	17	contrary	contrary	NOUN
fcis-985	35	18	,	,	PUNCT
fcis-985	35	19	the	the	PRON
fcis-985	35	20	smaller	small	ADJ
fcis-985	35	21	the	the	DET
fcis-985	35	22	q	q	NOUN
fcis-985	35	23	value	value	NOUN
fcis-985	35	24	,	,	PUNCT
fcis-985	35	25	the	the	DET
fcis-985	35	26	flatter	flatter	NOUN
fcis-985	35	27	the	the	DET
fcis-985	35	28	s	s	NOUN
fcis-985	35	29	-	-	PUNCT
fcis-985	35	30	shaped	shape	VERB
fcis-985	35	31	curve	curve	NOUN
fcis-985	35	32	;	;	PUNCT
fcis-985	35	33	generally	generally	ADV
fcis-985	35	34	,	,	PUNCT
fcis-985	35	35	take	take	VERB
fcis-985	35	36	q=1	q=1	PROPN
fcis-985	35	37	.	.	PUNCT
fcis-985	36	1	the	the	DET
fcis-985	36	2	specific	specific	ADJ
fcis-985	36	3	bp	bp	PROPN
fcis-985	36	4	algorithm	algorithm	NOUN
fcis-985	36	5	steps	step	NOUN
fcis-985	36	6	can	can	AUX
fcis-985	36	7	be	be	AUX
fcis-985	36	8	summarized	summarize	VERB
fcis-985	36	9	as	as	SCONJ
fcis-985	36	10	follows	follow	VERB
fcis-985	36	11	:	:	PUNCT
fcis-985	36	12	the	the	DET
fcis-985	36	13	first	first	ADJ
fcis-985	36	14	step	step	NOUN
fcis-985	36	15	is	be	AUX
fcis-985	36	16	to	to	PART
fcis-985	36	17	select	select	VERB
fcis-985	36	18	initial	initial	ADJ
fcis-985	36	19	weights	weight	NOUN
fcis-985	36	20	and	and	CCONJ
fcis-985	36	21	thresholds	threshold	NOUN
fcis-985	36	22	.	.	PUNCT
fcis-985	37	1	in	in	ADP
fcis-985	37	2	the	the	DET
fcis-985	37	3	second	second	ADJ
fcis-985	37	4	step	step	NOUN
fcis-985	37	5	,	,	PUNCT
fcis-985	37	6	the	the	DET
fcis-985	37	7	following	follow	VERB
fcis-985	37	8	process	process	NOUN
fcis-985	37	9	is	be	AUX
fcis-985	37	10	repeated	repeat	VERB
fcis-985	37	11	until	until	SCONJ
fcis-985	37	12	the	the	DET
fcis-985	37	13	performance	performance	NOUN
fcis-985	37	14	requirements	requirement	NOUN
fcis-985	37	15	are	be	AUX
fcis-985	37	16	met	meet	VERB
fcis-985	37	17	:	:	PUNCT
fcis-985	38	1	1.for	1.for	ADP
fcis-985	38	2	learning	learn	VERB
fcis-985	38	3	samples	sample	NOUN
fcis-985	38	4	p	p	NOUN
fcis-985	38	5	=	=	NOUN
fcis-985	38	6	1	1	NUM
fcis-985	38	7	to	to	ADP
fcis-985	38	8	n	n	PRON
fcis-985	38	9	1	1	NUM
fcis-985	38	10	)	)	PUNCT
fcis-985	38	11	calculate	calculate	VERB
fcis-985	38	12	the	the	DET
fcis-985	38	13	values	value	NOUN
fcis-985	38	14	of	of	ADP
fcis-985	38	15	yj	yj	PROPN
fcis-985	38	16	,	,	PUNCT
fcis-985	38	17	nutj	nutj	PROPN
fcis-985	38	18	and	and	CCONJ
fcis-985	38	19	o	o	PROPN
fcis-985	38	20	of	of	ADP
fcis-985	38	21	each	each	DET
fcis-985	38	22	node	node	PROPN
fcis-985	38	23	j	j	PROPN
fcis-985	38	24	in	in	ADP
fcis-985	38	25	each	each	DET
fcis-985	38	26	layer	layer	NOUN
fcis-985	38	27	(	(	PUNCT
fcis-985	38	28	forward	forward	ADJ
fcis-985	38	29	process	process	NOUN
fcis-985	38	30	)	)	PUNCT
fcis-985	38	31	;	;	PUNCT
fcis-985	39	1	2	2	X
fcis-985	39	2	)	)	PUNCT
fcis-985	39	3	for	for	ADP
fcis-985	39	4	each	each	DET
fcis-985	39	5	layer	layer	NOUN
fcis-985	39	6	from	from	ADP
fcis-985	39	7	the	the	DET
fcis-985	39	8	m	m	NOUN
fcis-985	39	9	layer	layer	NOUN
fcis-985	39	10	to	to	ADP
fcis-985	39	11	the	the	DET
fcis-985	39	12	second	second	ADJ
fcis-985	39	13	layer	layer	NOUN
fcis-985	39	14	,	,	PUNCT
fcis-985	39	15	for	for	ADP
fcis-985	39	16	each	each	DET
fcis-985	39	17	node	node	NOUN
fcis-985	39	18	of	of	ADP
fcis-985	39	19	each	each	DET
fcis-985	39	20	layer	layer	NOUN
fcis-985	39	21	,	,	PUNCT
fcis-985	39	22	reverse	reverse	ADJ
fcis-985	39	23	calculation	calculation	NOUN
fcis-985	39	24	(	(	PUNCT
fcis-985	39	25	reverse	reverse	ADJ
fcis-985	39	26	process	process	NOUN
fcis-985	39	27	)	)	PUNCT
fcis-985	39	28	;	;	PUNCT
fcis-985	39	29	2.correction	2.correction	NUM
fcis-985	39	30	weights	weight	NOUN
fcis-985	39	31	.	.	PUNCT
fcis-985	40	1	3	3	X
fcis-985	40	2	.	.	X
fcis-985	40	3	experimental	experimental	ADJ
fcis-985	40	4	tool	tool	NOUN
fcis-985	40	5	3.1	3.1	NUM
fcis-985	40	6	.	.	PUNCT
fcis-985	41	1	pycharm	pycharm	PROPN
fcis-985	41	2	pycharm	pycharm	NOUN
fcis-985	41	3	is	be	AUX
fcis-985	41	4	a	a	DET
fcis-985	41	5	python	python	NOUN
fcis-985	41	6	programming	programming	NOUN
fcis-985	41	7	ide	ide	NOUN
fcis-985	41	8	,	,	PUNCT
fcis-985	41	9	which	which	PRON
fcis-985	41	10	is	be	AUX
fcis-985	41	11	very	very	ADV
fcis-985	41	12	convenient	convenient	ADJ
fcis-985	41	13	to	to	PART
fcis-985	41	14	install	install	VERB
fcis-985	41	15	.	.	PUNCT
fcis-985	42	1	3.2	3.2	NUM
fcis-985	42	2	.	.	PUNCT
fcis-985	43	1	opencv	opencv	PROPN
fcis-985	43	2	opencv	opencv	PROPN
fcis-985	43	3	is	be	AUX
fcis-985	43	4	an	an	DET
fcis-985	43	5	important	important	ADJ
fcis-985	43	6	tool	tool	NOUN
fcis-985	43	7	for	for	ADP
fcis-985	43	8	pre	pre	ADJ
fcis-985	43	9	-	-	ADJ
fcis-985	43	10	learning	learning	NOUN
fcis-985	43	11	of	of	ADP
fcis-985	43	12	computer	computer	NOUN
fcis-985	43	13	vision	vision	NOUN
fcis-985	43	14	,	,	PUNCT
fcis-985	43	15	machine	machine	NOUN
fcis-985	43	16	vision	vision	NOUN
fcis-985	43	17	,	,	PUNCT
fcis-985	43	18	and	and	CCONJ
fcis-985	43	19	image	image	NOUN
fcis-985	43	20	processing	processing	NOUN
fcis-985	43	21	.	.	PUNCT
fcis-985	44	1	due	due	ADP
fcis-985	44	2	to	to	ADP
fcis-985	44	3	its	its	PRON
fcis-985	44	4	lightweight	lightweight	ADJ
fcis-985	44	5	and	and	CCONJ
fcis-985	44	6	efficient	efficient	ADJ
fcis-985	44	7	properties	property	NOUN
fcis-985	44	8	,	,	PUNCT
fcis-985	44	9	it	it	PRON
fcis-985	44	10	is	be	AUX
fcis-985	44	11	widely	widely	ADV
fcis-985	44	12	used	use	VERB
fcis-985	44	13	to	to	PART
fcis-985	44	14	solve	solve	VERB
fcis-985	44	15	computer	computer	NOUN
fcis-985	44	16	problems	problem	NOUN
fcis-985	44	17	in	in	ADP
fcis-985	44	18	various	various	ADJ
fcis-985	44	19	fields	field	NOUN
fcis-985	44	20	.	.	PUNCT
fcis-985	45	1	this	this	DET
fcis-985	45	2	paper	paper	NOUN
fcis-985	45	3	implements	implement	NOUN
fcis-985	45	4	face	face	VERB
fcis-985	45	5	recognition	recognition	NOUN
fcis-985	45	6	and	and	CCONJ
fcis-985	45	7	gender	gender	NOUN
fcis-985	45	8	detection	detection	NOUN
fcis-985	45	9	by	by	ADP
fcis-985	45	10	using	use	VERB
fcis-985	45	11	the	the	DET
fcis-985	45	12	python	python	NOUN
fcis-985	45	13	interface	interface	NOUN
fcis-985	45	14	in	in	ADP
fcis-985	45	15	opencv	opencv	PROPN
fcis-985	45	16	.	.	PROPN
fcis-985	46	1	4	4	NUM
fcis-985	46	2	.	.	PUNCT
fcis-985	46	3	experiment	experiment	NOUN
fcis-985	46	4	procedure	procedure	NOUN
fcis-985	46	5	the	the	DET
fcis-985	46	6	input	input	NOUN
fcis-985	46	7	matrix	matrix	NOUN
fcis-985	46	8	is	be	AUX
fcis-985	46	9	a	a	DET
fcis-985	46	10	large	large	ADJ
fcis-985	46	11	matrix	matrix	NOUN
fcis-985	46	12	,	,	PUNCT
fcis-985	46	13	and	and	CCONJ
fcis-985	46	14	each	each	DET
fcis-985	46	15	column	column	NOUN
fcis-985	46	16	in	in	ADP
fcis-985	46	17	the	the	DET
fcis-985	46	18	matrix	matrix	NOUN
fcis-985	46	19	represents	represent	VERB
fcis-985	46	20	a	a	DET
fcis-985	46	21	photo	photo	NOUN
fcis-985	46	22	.	.	PUNCT
fcis-985	47	1	for	for	ADP
fcis-985	47	2	example	example	NOUN
fcis-985	47	3	,	,	PUNCT
fcis-985	47	4	we	we	PRON
fcis-985	47	5	have	have	VERB
fcis-985	47	6	200	200	NUM
fcis-985	47	7	photos	photo	NOUN
fcis-985	47	8	,	,	PUNCT
fcis-985	47	9	and	and	CCONJ
fcis-985	47	10	each	each	DET
fcis-985	47	11	photo	photo	NOUN
fcis-985	47	12	has	have	VERB
fcis-985	47	13	10	10	NUM
fcis-985	47	14	*	*	NUM
fcis-985	47	15	10=100	10=100	NUM
fcis-985	47	16	pixels	pixel	NOUN
fcis-985	47	17	,	,	PUNCT
fcis-985	47	18	then	then	ADV
fcis-985	47	19	this	this	DET
fcis-985	47	20	matrix	matrix	NOUN
fcis-985	47	21	is	be	AUX
fcis-985	47	22	a	a	DET
fcis-985	47	23	100	100	NUM
fcis-985	47	24	*	*	NUM
fcis-985	47	25	200	200	NUM
fcis-985	47	26	matrix	matrix	NOUN
fcis-985	47	27	.	.	PUNCT
fcis-985	48	1	to	to	PART
fcis-985	48	2	get	get	VERB
fcis-985	48	3	these	these	DET
fcis-985	48	4	data	datum	NOUN
fcis-985	48	5	,	,	PUNCT
fcis-985	48	6	our	our	PRON
fcis-985	48	7	first	first	ADJ
fcis-985	48	8	step	step	NOUN
fcis-985	48	9	is	be	AUX
fcis-985	48	10	to	to	PART
fcis-985	48	11	find	find	VERB
fcis-985	48	12	a	a	DET
fcis-985	48	13	face	face	NOUN
fcis-985	48	14	recognition	recognition	NOUN
fcis-985	48	15	library	library	NOUN
fcis-985	48	16	.	.	PUNCT
fcis-985	49	1	there	there	PRON
fcis-985	49	2	are	be	VERB
fcis-985	49	3	many	many	ADJ
fcis-985	49	4	open	open	ADJ
fcis-985	49	5	source	source	NOUN
fcis-985	49	6	face	face	NOUN
fcis-985	49	7	recognition	recognition	NOUN
fcis-985	49	8	libraries	library	NOUN
fcis-985	49	9	from	from	ADP
fcis-985	49	10	which	which	PRON
fcis-985	49	11	you	you	PRON
fcis-985	49	12	can	can	AUX
fcis-985	49	13	get	get	VERB
fcis-985	49	14	all	all	DET
fcis-985	49	15	kinds	kind	NOUN
fcis-985	49	16	of	of	ADP
fcis-985	49	17	photos	photo	NOUN
fcis-985	49	18	you	you	PRON
fcis-985	49	19	want	want	VERB
fcis-985	49	20	.	.	PUNCT
fcis-985	50	1	after	after	ADP
fcis-985	50	2	obtaining	obtain	VERB
fcis-985	50	3	the	the	DET
fcis-985	50	4	photo	photo	NOUN
fcis-985	50	5	,	,	PUNCT
fcis-985	50	6	we	we	PRON
fcis-985	50	7	first	first	ADV
fcis-985	50	8	need	need	VERB
fcis-985	50	9	to	to	PART
fcis-985	50	10	identify	identify	VERB
fcis-985	50	11	the	the	DET
fcis-985	50	12	face	face	NOUN
fcis-985	50	13	.	.	PUNCT
fcis-985	51	1	this	this	DET
fcis-985	51	2	function	function	NOUN
fcis-985	51	3	here	here	ADV
fcis-985	51	4	will	will	AUX
fcis-985	51	5	identify	identify	VERB
fcis-985	51	6	the	the	DET
fcis-985	51	7	face	face	NOUN
fcis-985	51	8	and	and	CCONJ
fcis-985	51	9	then	then	ADV
fcis-985	51	10	grayscale	grayscale	VERB
fcis-985	51	11	it	it	PRON
fcis-985	51	12	and	and	CCONJ
fcis-985	51	13	re	re	VERB
fcis-985	51	14	-	-	VERB
fcis-985	51	15	save	save	VERB
fcis-985	51	16	it	it	PRON
fcis-985	51	17	as	as	ADP
fcis-985	51	18	a	a	DET
fcis-985	51	19	numbered	numbered	ADJ
fcis-985	51	20	photo	photo	NOUN
fcis-985	51	21	.	.	PUNCT
fcis-985	52	1	since	since	SCONJ
fcis-985	52	2	the	the	DET
fcis-985	52	3	pixel	pixel	PROPN
fcis-985	52	4	value	value	NOUN
fcis-985	52	5	of	of	ADP
fcis-985	52	6	the	the	DET
fcis-985	52	7	recognized	recognize	VERB
fcis-985	52	8	face	face	NOUN
fcis-985	52	9	is	be	AUX
fcis-985	52	10	too	too	ADV
fcis-985	52	11	large	large	ADJ
fcis-985	52	12	,	,	PUNCT
fcis-985	52	13	and	and	CCONJ
fcis-985	52	14	different	different	ADJ
fcis-985	52	15	photos	photo	NOUN
fcis-985	52	16	contain	contain	VERB
fcis-985	52	17	different	different	ADJ
fcis-985	52	18	pixel	pixel	NOUN
fcis-985	52	19	values	value	NOUN
fcis-985	52	20	,	,	PUNCT
fcis-985	52	21	we	we	PRON
fcis-985	52	22	need	need	VERB
fcis-985	52	23	to	to	PART
fcis-985	52	24	unify	unify	VERB
fcis-985	52	25	the	the	DET
fcis-985	52	26	pixel	pixel	ADJ
fcis-985	52	27	values	value	NOUN
fcis-985	52	28	of	of	ADP
fcis-985	52	29	each	each	DET
fcis-985	52	30	photo	photo	NOUN
fcis-985	52	31	to	to	PART
fcis-985	52	32	obtain	obtain	VERB
fcis-985	52	33	a	a	DET
fcis-985	52	34	grayscale	grayscale	NOUN
fcis-985	52	35	image	image	NOUN
fcis-985	52	36	with	with	ADP
fcis-985	52	37	uniform	uniform	ADJ
fcis-985	52	38	pixel	pixel	PROPN
fcis-985	52	39	values	value	NOUN
fcis-985	52	40	,	,	PUNCT
fcis-985	52	41	we	we	PRON
fcis-985	52	42	need	need	VERB
fcis-985	52	43	to	to	PART
fcis-985	52	44	get	get	VERB
fcis-985	52	45	all	all	DET
fcis-985	52	46	the	the	DET
fcis-985	52	47	images	image	NOUN
fcis-985	52	48	into	into	ADP
fcis-985	52	49	one	one	NUM
fcis-985	52	50	inside	inside	ADP
fcis-985	52	51	the	the	DET
fcis-985	52	52	large	large	ADJ
fcis-985	52	53	matrix	matrix	NOUN
fcis-985	52	54	as	as	ADP
fcis-985	52	55	input	input	NOUN
fcis-985	52	56	to	to	ADP
fcis-985	52	57	the	the	DET
fcis-985	52	58	core	core	NOUN
fcis-985	52	59	function	function	NOUN
fcis-985	52	60	this	this	DET
fcis-985	52	61	function	function	NOUN
fcis-985	52	62	returns	return	VERB
fcis-985	52	63	a	a	DET
fcis-985	52	64	matrix	matrix	NOUN
fcis-985	52	65	.	.	PUNCT
fcis-985	53	1	this	this	PRON
fcis-985	53	2	completes	complete	VERB
fcis-985	53	3	the	the	DET
fcis-985	53	4	entire	entire	ADJ
fcis-985	53	5	data	datum	NOUN
fcis-985	53	6	processing	processing	NOUN
fcis-985	53	7	.	.	PUNCT
fcis-985	54	1	in	in	ADP
fcis-985	54	2	the	the	DET
fcis-985	54	3	first	first	ADJ
fcis-985	54	4	step	step	NOUN
fcis-985	54	5	,	,	PUNCT
fcis-985	54	6	we	we	PRON
fcis-985	54	7	need	need	VERB
fcis-985	54	8	to	to	PART
fcis-985	54	9	detect	detect	VERB
fcis-985	54	10	the	the	DET
fcis-985	54	11	face	face	NOUN
fcis-985	54	12	.	.	PUNCT
fcis-985	55	1	determine	determine	VERB
fcis-985	55	2	if	if	SCONJ
fcis-985	55	3	a	a	DET
fcis-985	55	4	face	face	NOUN
fcis-985	55	5	is	be	AUX
fcis-985	55	6	included	include	VERB
fcis-985	55	7	,	,	PUNCT
fcis-985	55	8	and	and	CCONJ
fcis-985	55	9	if	if	SCONJ
fcis-985	55	10	so	so	ADV
fcis-985	55	11	,	,	PUNCT
fcis-985	55	12	determine	determine	VERB
fcis-985	55	13	the	the	DET
fcis-985	55	14	location	location	NOUN
fcis-985	55	15	and	and	CCONJ
fcis-985	55	16	size	size	NOUN
fcis-985	55	17	of	of	ADP
fcis-985	55	18	the	the	DET
fcis-985	55	19	face	face	NOUN
fcis-985	55	20	.	.	PUNCT
fcis-985	56	1	because	because	SCONJ
fcis-985	56	2	the	the	DET
fcis-985	56	3	acquired	acquire	VERB
fcis-985	56	4	images	image	NOUN
fcis-985	56	5	are	be	AUX
fcis-985	56	6	all	all	PRON
fcis-985	56	7	color	color	NOUN
fcis-985	56	8	images	image	NOUN
fcis-985	56	9	,	,	PUNCT
fcis-985	56	10	skin	skin	NOUN
fcis-985	56	11	color	color	NOUN
fcis-985	56	12	detection	detection	NOUN
fcis-985	56	13	can	can	AUX
fcis-985	56	14	be	be	AUX
fcis-985	56	15	performed	perform	VERB
fcis-985	56	16	first	first	ADV
fcis-985	56	17	.	.	PUNCT
fcis-985	57	1	after	after	ADP
fcis-985	57	2	detecting	detect	VERB
fcis-985	57	3	the	the	DET
fcis-985	57	4	skin	skin	NOUN
fcis-985	57	5	color	color	NOUN
fcis-985	57	6	pixels	pixel	NOUN
fcis-985	57	7	,	,	PUNCT
fcis-985	57	8	it	it	PRON
fcis-985	57	9	is	be	AUX
fcis-985	57	10	necessary	necessary	ADJ
fcis-985	57	11	to	to	PART
fcis-985	57	12	segment	segment	VERB
fcis-985	57	13	the	the	DET
fcis-985	57	14	possible	possible	ADJ
fcis-985	57	15	face	face	NOUN
fcis-985	57	16	regions	region	NOUN
fcis-985	57	17	according	accord	VERB
fcis-985	57	18	to	to	ADP
fcis-985	57	19	their	their	PRON
fcis-985	57	20	chromatic	chromatic	ADJ
fcis-985	57	21	similarity	similarity	NOUN
fcis-985	57	22	and	and	CCONJ
fcis-985	57	23	spatial	spatial	ADJ
fcis-985	57	24	correlation	correlation	NOUN
fcis-985	57	25	,	,	PUNCT
fcis-985	57	26	and	and	CCONJ
fcis-985	57	27	at	at	ADP
fcis-985	57	28	the	the	DET
fcis-985	57	29	same	same	ADJ
fcis-985	57	30	time	time	NOUN
fcis-985	57	31	,	,	PUNCT
fcis-985	57	32	use	use	VERB
fcis-985	57	33	the	the	DET
fcis-985	57	34	geometric	geometric	ADJ
fcis-985	57	35	features	feature	NOUN
fcis-985	57	36	or	or	CCONJ
fcis-985	57	37	grayscale	grayscale	NOUN
fcis-985	57	38	features	feature	NOUN
fcis-985	57	39	of	of	ADP
fcis-985	57	40	the	the	DET
fcis-985	57	41	regions	region	NOUN
fcis-985	57	42	to	to	PART
fcis-985	57	43	verify	verify	VERB
fcis-985	57	44	whether	whether	SCONJ
fcis-985	57	45	it	it	PRON
fcis-985	57	46	is	be	AUX
fcis-985	57	47	a	a	DET
fcis-985	57	48	human	human	ADJ
fcis-985	57	49	face	face	NOUN
fcis-985	57	50	.	.	PUNCT
fcis-985	58	1	other	other	ADJ
fcis-985	58	2	objects	object	NOUN
fcis-985	58	3	with	with	ADP
fcis-985	58	4	a	a	DET
fcis-985	58	5	color	color	NOUN
fcis-985	58	6	similar	similar	ADJ
fcis-985	58	7	to	to	ADP
fcis-985	58	8	skin	skin	NOUN
fcis-985	58	9	tones	tone	NOUN
fcis-985	58	10	are	be	AUX
fcis-985	58	11	excluded	exclude	VERB
fcis-985	58	12	.	.	PUNCT
fcis-985	59	1	here	here	ADV
fcis-985	59	2	we	we	PRON
fcis-985	59	3	use	use	VERB
fcis-985	59	4	the	the	DET
fcis-985	59	5	classifier	classifier	NOUN
fcis-985	59	6	that	that	PRON
fcis-985	59	7	comes	come	VERB
fcis-985	59	8	with	with	SCONJ
fcis-985	59	9	opencv	opencv	PROPN
fcis-985	59	10	to	to	PART
fcis-985	59	11	identify	identify	VERB
fcis-985	59	12	the	the	DET
fcis-985	59	13	face	face	NOUN
fcis-985	59	14	and	and	CCONJ
fcis-985	59	15	draw	draw	VERB
fcis-985	59	16	a	a	DET
fcis-985	59	17	frame	frame	NOUN
fcis-985	59	18	to	to	PART
fcis-985	59	19	display	display	VERB
fcis-985	59	20	the	the	DET
fcis-985	59	21	face	face	NOUN
fcis-985	59	22	area	area	NOUN
fcis-985	59	23	.	.	PUNCT
fcis-985	60	1	the	the	DET
fcis-985	60	2	second	second	ADJ
fcis-985	60	3	step	step	NOUN
fcis-985	60	4	is	be	AUX
fcis-985	60	5	to	to	PART
fcis-985	60	6	grayscale	grayscale	VERB
fcis-985	60	7	the	the	DET
fcis-985	60	8	read	read	NOUN
fcis-985	60	9	image	image	NOUN
fcis-985	60	10	,	,	PUNCT
fcis-985	60	11	because	because	SCONJ
fcis-985	60	12	opencv	opencv	ADJ
fcis-985	60	13	processing	processing	NOUN
fcis-985	60	14	is	be	AUX
fcis-985	60	15	to	to	PART
fcis-985	60	16	process	process	VERB
fcis-985	60	17	grayscale	grayscale	NOUN
fcis-985	60	18	photos	photo	NOUN
fcis-985	60	19	.	.	PUNCT
fcis-985	61	1	the	the	DET
fcis-985	61	2	purpose	purpose	NOUN
fcis-985	61	3	of	of	ADP
fcis-985	61	4	image	image	NOUN
fcis-985	61	5	processing	processing	NOUN
fcis-985	61	6	is	be	AUX
fcis-985	61	7	to	to	PART
fcis-985	61	8	facilitate	facilitate	VERB
fcis-985	61	9	the	the	DET
fcis-985	61	10	extraction	extraction	NOUN
fcis-985	61	11	of	of	ADP
fcis-985	61	12	the	the	DET
fcis-985	61	13	eigenvalues	eigenvalue	NOUN
fcis-985	61	14	of	of	ADP
fcis-985	61	15	the	the	DET
fcis-985	61	16	face	face	NOUN
fcis-985	61	17	,	,	PUNCT
fcis-985	61	18	and	and	CCONJ
fcis-985	61	19	then	then	ADV
fcis-985	61	20	to	to	PART
fcis-985	61	21	compare	compare	VERB
fcis-985	61	22	and	and	CCONJ
fcis-985	61	23	identify	identify	VERB
fcis-985	61	24	,	,	PUNCT
fcis-985	61	25	so	so	SCONJ
fcis-985	61	26	this	this	DET
fcis-985	61	27	step	step	NOUN
fcis-985	61	28	is	be	AUX
fcis-985	61	29	also	also	ADV
fcis-985	61	30	very	very	ADV
fcis-985	61	31	important	important	ADJ
fcis-985	61	32	.	.	PUNCT
fcis-985	62	1	first	first	ADV
fcis-985	62	2	,	,	PUNCT
fcis-985	62	3	for	for	ADP
fcis-985	62	4	the	the	DET
fcis-985	62	5	segmented	segment	VERB
fcis-985	62	6	face	face	NOUN
fcis-985	62	7	,	,	PUNCT
fcis-985	62	8	since	since	SCONJ
fcis-985	62	9	noise	noise	NOUN
fcis-985	62	10	brings	bring	VERB
fcis-985	62	11	distortion	distortion	NOUN
fcis-985	62	12	and	and	CCONJ
fcis-985	62	13	degradation	degradation	NOUN
fcis-985	62	14	,	,	PUNCT
fcis-985	62	15	it	it	PRON
fcis-985	62	16	is	be	AUX
fcis-985	62	17	a	a	DET
fcis-985	62	18	necessary	necessary	ADJ
fcis-985	62	19	step	step	NOUN
fcis-985	62	20	to	to	PART
fcis-985	62	21	use	use	VERB
fcis-985	62	22	filtering	filter	VERB
fcis-985	62	23	to	to	PART
fcis-985	62	24	remove	remove	VERB
fcis-985	62	25	noise	noise	NOUN
fcis-985	62	26	before	before	ADP
fcis-985	62	27	feature	feature	NOUN
fcis-985	62	28	extraction	extraction	NOUN
fcis-985	62	29	.	.	PUNCT
fcis-985	63	1	the	the	DET
fcis-985	63	2	second	second	ADJ
fcis-985	63	3	scale	scale	NOUN
fcis-985	63	4	normalization	normalization	NOUN
fcis-985	63	5	,	,	PUNCT
fcis-985	63	6	the	the	DET
fcis-985	63	7	idea	idea	NOUN
fcis-985	63	8	is	be	AUX
fcis-985	63	9	to	to	PART
fcis-985	63	10	transform	transform	VERB
fcis-985	63	11	the	the	DET
fcis-985	63	12	face	face	NOUN
fcis-985	63	13	images	image	NOUN
fcis-985	63	14	of	of	ADP
fcis-985	63	15	different	different	ADJ
fcis-985	63	16	sizes	size	NOUN
fcis-985	63	17	into	into	ADP
fcis-985	63	18	a	a	DET
fcis-985	63	19	unified	unify	VERB
fcis-985	63	20	standard	standard	ADJ
fcis-985	63	21	size	size	NOUN
fcis-985	63	22	image	image	NOUN
fcis-985	63	23	to	to	PART
fcis-985	63	24	facilitate	facilitate	VERB
fcis-985	63	25	the	the	DET
fcis-985	63	26	extraction	extraction	NOUN
fcis-985	63	27	of	of	ADP
fcis-985	63	28	face	face	NOUN
fcis-985	63	29	features	feature	NOUN
fcis-985	63	30	.	.	PUNCT
fcis-985	64	1	the	the	DET
fcis-985	64	2	third	third	ADJ
fcis-985	64	3	grayscale	grayscale	NOUN
fcis-985	64	4	normalization	normalization	NOUN
fcis-985	64	5	,	,	PUNCT
fcis-985	64	6	the	the	DET
fcis-985	64	7	research	research	NOUN
fcis-985	64	8	of	of	ADP
fcis-985	64	9	face	face	NOUN
fcis-985	64	10	recognition	recognition	NOUN
fcis-985	64	11	generally	generally	ADV
fcis-985	64	12	takes	take	VERB
fcis-985	64	13	grayscale	grayscale	NOUN
fcis-985	64	14	images	image	NOUN
fcis-985	64	15	as	as	ADP
fcis-985	64	16	the	the	DET
fcis-985	64	17	research	research	NOUN
fcis-985	64	18	object	object	NOUN
fcis-985	64	19	.	.	PUNCT
fcis-985	65	1	for	for	ADP
fcis-985	65	2	color	color	NOUN
fcis-985	65	3	face	face	NOUN
fcis-985	65	4	images	image	NOUN
fcis-985	65	5	,	,	PUNCT
fcis-985	65	6	grayscale	grayscale	NOUN
fcis-985	65	7	processing	processing	NOUN
fcis-985	65	8	can	can	AUX
fcis-985	65	9	be	be	AUX
fcis-985	65	10	performed	perform	VERB
fcis-985	65	11	first	first	ADV
fcis-985	65	12	.	.	PUNCT
fcis-985	66	1	the	the	DET
fcis-985	66	2	fourth	fourth	ADJ
fcis-985	66	3	grayscale	grayscale	NOUN
fcis-985	66	4	equalization	equalization	NOUN
fcis-985	66	5	,	,	PUNCT
fcis-985	66	6	because	because	SCONJ
fcis-985	66	7	the	the	DET
fcis-985	66	8	change	change	NOUN
fcis-985	66	9	of	of	ADP
fcis-985	66	10	illumination	illumination	NOUN
fcis-985	66	11	during	during	ADP
fcis-985	66	12	image	image	NOUN
fcis-985	66	13	acquisition	acquisition	NOUN
fcis-985	66	14	easily	easily	ADV
fcis-985	66	15	leads	lead	VERB
fcis-985	66	16	to	to	ADP
fcis-985	66	17	the	the	DET
fcis-985	66	18	image	image	NOUN
fcis-985	66	19	showing	show	VERB
fcis-985	66	20	different	different	ADJ
fcis-985	66	21	degrees	degree	NOUN
fcis-985	66	22	of	of	ADP
fcis-985	66	23	brightness	brightness	NOUN
fcis-985	66	24	and	and	CCONJ
fcis-985	66	25	darkness	darkness	NOUN
fcis-985	66	26	,	,	PUNCT
fcis-985	66	27	so	so	SCONJ
fcis-985	66	28	it	it	PRON
fcis-985	66	29	is	be	AUX
fcis-985	66	30	necessary	necessary	ADJ
fcis-985	66	31	to	to	PART
fcis-985	66	32	perform	perform	VERB
fcis-985	66	33	grayscale	grayscale	NOUN
fcis-985	66	34	equalization	equalization	NOUN
fcis-985	66	35	processing	processing	NOUN
fcis-985	66	36	on	on	ADP
fcis-985	66	37	the	the	DET
fcis-985	66	38	face	face	NOUN
fcis-985	66	39	image	image	NOUN
fcis-985	66	40	.	.	PUNCT
fcis-985	67	1	the	the	DET
fcis-985	67	2	function	function	NOUN
fcis-985	67	3	of	of	ADP
fcis-985	67	4	gray	gray	ADJ
fcis-985	67	5	level	level	NOUN
fcis-985	67	6	equalization	equalization	NOUN
fcis-985	67	7	is	be	AUX
fcis-985	67	8	to	to	PART
fcis-985	67	9	enhance	enhance	VERB
fcis-985	67	10	the	the	DET
fcis-985	67	11	overall	overall	ADJ
fcis-985	67	12	contrast	contrast	NOUN
fcis-985	67	13	of	of	ADP
fcis-985	67	14	the	the	DET
fcis-985	67	15	face	face	NOUN
fcis-985	67	16	image	image	NOUN
fcis-985	67	17	and	and	CCONJ
fcis-985	67	18	make	make	VERB
fcis-985	67	19	the	the	DET
fcis-985	67	20	gray	gray	ADJ
fcis-985	67	21	distribution	distribution	NOUN
fcis-985	67	22	uniform	uniform	NOUN
fcis-985	67	23	to	to	PART
fcis-985	67	24	eliminate	eliminate	VERB
fcis-985	67	25	the	the	DET
fcis-985	67	26	influence	influence	NOUN
fcis-985	67	27	of	of	ADP
fcis-985	67	28	illumination	illumination	NOUN
fcis-985	67	29	changes	change	NOUN
fcis-985	67	30	.	.	PUNCT
fcis-985	68	1	step	step	NOUN
fcis-985	68	2	3	3	NUM
fcis-985	68	3	draw	draw	VERB
fcis-985	68	4	the	the	DET
fcis-985	68	5	box	box	NOUN
fcis-985	68	6	and	and	CCONJ
fcis-985	68	7	save	save	VERB
fcis-985	68	8	it	it	PRON
fcis-985	68	9	.	.	PUNCT
fcis-985	69	1	face	face	VERB
fcis-985	69	2	feature	feature	NOUN
fcis-985	69	3	extraction	extraction	NOUN
fcis-985	69	4	and	and	CCONJ
fcis-985	69	5	recognition	recognition	NOUN
fcis-985	69	6	are	be	AUX
fcis-985	69	7	the	the	DET
fcis-985	69	8	two	two	NUM
fcis-985	69	9	most	most	ADV
fcis-985	69	10	critical	critical	ADJ
fcis-985	69	11	issues	issue	NOUN
fcis-985	69	12	in	in	ADP
fcis-985	69	13	face	face	NOUN
fcis-985	69	14	recognition	recognition	NOUN
fcis-985	69	15	research	research	NOUN
fcis-985	69	16	.	.	PUNCT
fcis-985	70	1	face	face	NOUN
fcis-985	70	2	feature	feature	NOUN
fcis-985	70	3	extraction	extraction	NOUN
fcis-985	70	4	,	,	PUNCT
fcis-985	70	5	also	also	ADV
fcis-985	70	6	known	know	VERB
fcis-985	70	7	as	as	ADP
fcis-985	70	8	face	face	NOUN
fcis-985	70	9	description	description	NOUN
fcis-985	70	10	,	,	PUNCT
fcis-985	70	11	is	be	AUX
fcis-985	70	12	a	a	DET
fcis-985	70	13	process	process	NOUN
fcis-985	70	14	of	of	ADP
fcis-985	70	15	extracting	extract	VERB
fcis-985	70	16	various	various	ADJ
fcis-985	70	17	face	face	NOUN
fcis-985	70	18	features	feature	NOUN
fcis-985	70	19	based	base	VERB
fcis-985	70	20	on	on	ADP
fcis-985	70	21	image	image	NOUN
fcis-985	70	22	preprocessing	preprocessing	NOUN
fcis-985	70	23	such	such	ADJ
fcis-985	70	24	as	as	ADP
fcis-985	70	25	face	face	NOUN
fcis-985	70	26	detection	detection	NOUN
fcis-985	70	27	,	,	PUNCT
fcis-985	70	28	positioning	positioning	NOUN
fcis-985	70	29	,	,	PUNCT
fcis-985	70	30	and	and	CCONJ
fcis-985	70	31	normalization	normalization	NOUN
fcis-985	70	32	,	,	PUNCT
fcis-985	70	33	which	which	PRON
fcis-985	70	34	lays	lay	VERB
fcis-985	70	35	the	the	DET
fcis-985	70	36	foundation	foundation	NOUN
fcis-985	70	37	for	for	ADP
fcis-985	70	38	face	face	NOUN
fcis-985	70	39	recognition	recognition	NOUN
fcis-985	70	40	and	and	CCONJ
fcis-985	70	41	classification	classification	NOUN
fcis-985	70	42	.	.	PUNCT
fcis-985	71	1	after	after	SCONJ
fcis-985	71	2	feature	feature	NOUN
fcis-985	71	3	extraction	extraction	NOUN
fcis-985	71	4	is	be	AUX
fcis-985	71	5	the	the	DET
fcis-985	71	6	face	face	NOUN
fcis-985	71	7	recognition	recognition	NOUN
fcis-985	71	8	classification	classification	NOUN
fcis-985	71	9	process	process	NOUN
fcis-985	71	10	.	.	PUNCT
fcis-985	72	1	face	face	NOUN
fcis-985	72	2	recognition	recognition	NOUN
fcis-985	72	3	classification	classification	NOUN
fcis-985	72	4	is	be	AUX
fcis-985	72	5	the	the	DET
fcis-985	72	6	last	last	ADJ
fcis-985	72	7	step	step	NOUN
fcis-985	72	8	of	of	ADP
fcis-985	72	9	the	the	DET
fcis-985	72	10	entire	entire	ADJ
fcis-985	72	11	face	face	NOUN
fcis-985	72	12	recognition	recognition	NOUN
fcis-985	72	13	.	.	PUNCT
fcis-985	73	1	its	its	PRON
fcis-985	73	2	main	main	ADJ
fcis-985	73	3	task	task	NOUN
fcis-985	73	4	is	be	AUX
fcis-985	73	5	to	to	PART
fcis-985	73	6	compare	compare	VERB
fcis-985	73	7	the	the	DET
fcis-985	73	8	face	face	NOUN
fcis-985	73	9	data	datum	NOUN
fcis-985	73	10	to	to	PART
fcis-985	73	11	be	be	AUX
fcis-985	73	12	measured	measure	VERB
fcis-985	73	13	according	accord	VERB
fcis-985	73	14	to	to	ADP
fcis-985	73	15	the	the	DET
fcis-985	73	16	extraction	extraction	NOUN
fcis-985	73	17	results	result	NOUN
fcis-985	73	18	of	of	ADP
fcis-985	73	19	facial	facial	ADJ
fcis-985	73	20	features	feature	NOUN
fcis-985	73	21	,	,	PUNCT
fcis-985	73	22	and	and	CCONJ
fcis-985	73	23	judge	judge	NOUN
fcis-985	73	24	according	accord	VERB
fcis-985	73	25	to	to	ADP
fcis-985	73	26	the	the	DET
fcis-985	73	27	degree	degree	NOUN
fcis-985	73	28	of	of	ADP
fcis-985	73	29	similarity	similarity	NOUN
fcis-985	73	30	.	.	PUNCT
fcis-985	74	1	find	find	VERB
fcis-985	74	2	out	out	ADP
fcis-985	74	3	whether	whether	SCONJ
fcis-985	74	4	the	the	DET
fcis-985	74	5	face	face	NOUN
fcis-985	74	6	to	to	PART
fcis-985	74	7	be	be	AUX
fcis-985	74	8	tested	test	VERB
fcis-985	74	9	is	be	AUX
fcis-985	74	10	the	the	DET
fcis-985	74	11	same	same	ADJ
fcis-985	74	12	person	person	NOUN
fcis-985	74	13	.	.	PUNCT
fcis-985	75	1	the	the	DET
fcis-985	75	2	final	final	ADJ
fcis-985	75	3	result	result	NOUN
fcis-985	75	4	of	of	ADP
fcis-985	75	5	face	face	NOUN
fcis-985	75	6	detection	detection	NOUN
fcis-985	75	7	is	be	AUX
fcis-985	75	8	shown	show	VERB
fcis-985	75	9	in	in	ADP
fcis-985	75	10	figure	figure	NOUN
fcis-985	75	11	1	1	NUM
fcis-985	75	12	.	.	PUNCT
fcis-985	76	1	figure	figure	NOUN
fcis-985	76	2	1	1	NUM
fcis-985	76	3	.	.	NOUN
fcis-985	76	4	face	face	NOUN
fcis-985	76	5	detection	detection	NOUN
fcis-985	76	6	result	result	VERB
fcis-985	76	7	the	the	DET
fcis-985	76	8	input	input	NOUN
fcis-985	76	9	of	of	ADP
fcis-985	76	10	the	the	DET
fcis-985	76	11	core	core	NOUN
fcis-985	76	12	function	function	NOUN
fcis-985	76	13	of	of	ADP
fcis-985	76	14	gender	gender	NOUN
fcis-985	76	15	detection	detection	NOUN
fcis-985	76	16	has	have	VERB
fcis-985	76	17	two	two	NUM
fcis-985	76	18	matrices	matrix	NOUN
fcis-985	76	19	,	,	PUNCT
fcis-985	76	20	one	one	NUM
fcis-985	76	21	is	be	AUX
fcis-985	76	22	the	the	DET
fcis-985	76	23	sample	sample	NOUN
fcis-985	76	24	data	data	NOUN
fcis-985	76	25	,	,	PUNCT
fcis-985	76	26	the	the	DET
fcis-985	76	27	other	other	ADJ
fcis-985	76	28	is	be	AUX
fcis-985	76	29	the	the	DET
fcis-985	76	30	sample	sample	NOUN
fcis-985	76	31	label	label	NOUN
fcis-985	76	32	,	,	PUNCT
fcis-985	76	33	where	where	SCONJ
fcis-985	76	34	the	the	DET
fcis-985	76	35	sample	sample	NOUN
fcis-985	76	36	data	data	NOUN
fcis-985	76	37	is	be	AUX
fcis-985	76	38	the	the	DET
fcis-985	76	39	pixel	pixel	PROPN
fcis-985	76	40	value	value	NOUN
fcis-985	76	41	,	,	PUNCT
fcis-985	76	42	the	the	DET
fcis-985	76	43	sample	sample	NOUN
fcis-985	76	44	label	label	NOUN
fcis-985	76	45	is	be	AUX
fcis-985	76	46	artificially	artificially	ADV
fcis-985	76	47	set	set	VERB
fcis-985	76	48	,	,	PUNCT
fcis-985	76	49	we	we	PRON
fcis-985	76	50	set	set	VERB
fcis-985	76	51	0	0	NUM
fcis-985	76	52	for	for	ADP
fcis-985	76	53	girls	girl	NOUN
fcis-985	76	54	and	and	CCONJ
fcis-985	76	55	1	1	NUM
fcis-985	76	56	for	for	ADP
fcis-985	76	57	boys	boy	NOUN
fcis-985	76	58	.	.	PUNCT
fcis-985	77	1	first	first	ADV
fcis-985	77	2	,	,	PUNCT
fcis-985	77	3	the	the	DET
fcis-985	77	4	function	function	NOUN
fcis-985	77	5	requires	require	VERB
fcis-985	77	6	us	we	PRON
fcis-985	77	7	to	to	PART
fcis-985	77	8	manually	manually	ADV
fcis-985	77	9	enter	enter	VERB
fcis-985	77	10	several	several	ADJ
fcis-985	77	11	parameters	parameter	NOUN
fcis-985	77	12	,	,	PUNCT
fcis-985	77	13	namely	namely	ADV
fcis-985	77	14	the	the	DET
fcis-985	77	15	number	number	NOUN
fcis-985	77	16	of	of	ADP
fcis-985	77	17	iteration	iteration	NOUN
fcis-985	77	18	steps	step	NOUN
fcis-985	77	19	,	,	PUNCT
fcis-985	77	20	the	the	DET
fcis-985	77	21	learning	learning	NOUN
fcis-985	77	22	factor	factor	NOUN
fcis-985	77	23	,	,	PUNCT
fcis-985	77	24	and	and	CCONJ
fcis-985	77	25	the	the	DET
fcis-985	77	26	number	number	NOUN
fcis-985	77	27	of	of	ADP
fcis-985	77	28	hidden	hidden	ADJ
fcis-985	77	29	neurons	neuron	NOUN
fcis-985	77	30	.	.	PUNCT
fcis-985	78	1	the	the	DET
fcis-985	78	2	number	number	NOUN
fcis-985	78	3	of	of	ADP
fcis-985	78	4	iteration	iteration	NOUN
fcis-985	78	5	steps	step	NOUN
fcis-985	78	6	3	3	NUM
fcis-985	78	7	will	will	AUX
fcis-985	78	8	affect	affect	VERB
fcis-985	78	9	our	our	PRON
fcis-985	78	10	final	final	ADJ
fcis-985	78	11	accuracy	accuracy	NOUN
fcis-985	78	12	and	and	CCONJ
fcis-985	78	13	the	the	DET
fcis-985	78	14	time	time	NOUN
fcis-985	78	15	required	require	VERB
fcis-985	78	16	for	for	ADP
fcis-985	78	17	learning	learn	VERB
fcis-985	78	18	.	.	PUNCT
fcis-985	79	1	the	the	DET
fcis-985	79	2	learning	learning	NOUN
fcis-985	79	3	factor	factor	NOUN
fcis-985	79	4	is	be	AUX
fcis-985	79	5	a	a	DET
fcis-985	79	6	parameter	parameter	NOUN
fcis-985	79	7	of	of	ADP
fcis-985	79	8	the	the	DET
fcis-985	79	9	backpropagation	backpropagation	NOUN
fcis-985	79	10	algorithm	algorithm	NOUN
fcis-985	79	11	,	,	PUNCT
fcis-985	79	12	and	and	CCONJ
fcis-985	79	13	the	the	DET
fcis-985	79	14	number	number	NOUN
fcis-985	79	15	of	of	ADP
fcis-985	79	16	hidden	hide	VERB
fcis-985	79	17	neurons	neuron	NOUN
fcis-985	79	18	is	be	AUX
fcis-985	79	19	related	relate	VERB
fcis-985	79	20	to	to	ADP
fcis-985	79	21	the	the	DET
fcis-985	79	22	number	number	NOUN
fcis-985	79	23	of	of	ADP
fcis-985	79	24	input	input	NOUN
fcis-985	79	25	pixels	pixel	NOUN
fcis-985	79	26	.	.	PUNCT
fcis-985	80	1	the	the	DET
fcis-985	80	2	next	next	ADJ
fcis-985	80	3	step	step	NOUN
fcis-985	80	4	is	be	AUX
fcis-985	80	5	to	to	PART
fcis-985	80	6	initialize	initialize	VERB
fcis-985	80	7	the	the	DET
fcis-985	80	8	parameters	parameter	NOUN
fcis-985	80	9	.	.	PUNCT
fcis-985	81	1	we	we	PRON
fcis-985	81	2	have	have	VERB
fcis-985	81	3	three	three	NUM
fcis-985	81	4	layers	layer	NOUN
fcis-985	81	5	in	in	ADP
fcis-985	81	6	total	total	ADJ
fcis-985	81	7	,	,	PUNCT
fcis-985	81	8	input	input	NOUN
fcis-985	81	9	layer	layer	NOUN
fcis-985	81	10	,	,	PUNCT
fcis-985	81	11	hidden	hide	VERB
fcis-985	81	12	layer	layer	NOUN
fcis-985	81	13	and	and	CCONJ
fcis-985	81	14	output	output	NOUN
fcis-985	81	15	layer	layer	NOUN
fcis-985	81	16	.	.	PUNCT
fcis-985	82	1	the	the	DET
fcis-985	82	2	number	number	NOUN
fcis-985	82	3	of	of	ADP
fcis-985	82	4	neurons	neuron	NOUN
fcis-985	82	5	in	in	ADP
fcis-985	82	6	the	the	DET
fcis-985	82	7	input	input	NOUN
fcis-985	82	8	layer	layer	NOUN
fcis-985	82	9	is	be	AUX
fcis-985	82	10	the	the	DET
fcis-985	82	11	number	number	NOUN
fcis-985	82	12	of	of	ADP
fcis-985	82	13	photo	photo	NOUN
fcis-985	82	14	pixels	pixel	NOUN
fcis-985	82	15	,	,	PUNCT
fcis-985	82	16	the	the	DET
fcis-985	82	17	output	output	NOUN
fcis-985	82	18	is	be	AUX
fcis-985	82	19	only	only	ADV
fcis-985	82	20	one	one	NUM
fcis-985	82	21	neuron	neuron	NOUN
fcis-985	82	22	,	,	PUNCT
fcis-985	82	23	and	and	CCONJ
fcis-985	82	24	the	the	DET
fcis-985	82	25	number	number	NOUN
fcis-985	82	26	of	of	ADP
fcis-985	82	27	hidden	hidden	ADJ
fcis-985	82	28	layers	layer	NOUN
fcis-985	82	29	is	be	AUX
fcis-985	82	30	artificially	artificially	ADV
fcis-985	82	31	set	set	VERB
fcis-985	82	32	.	.	PUNCT
fcis-985	83	1	figure	figure	NOUN
fcis-985	83	2	2	2	NUM
fcis-985	83	3	.	.	PUNCT
fcis-985	83	4	gender	gender	NOUN
fcis-985	83	5	test	test	NOUN
fcis-985	83	6	results	result	VERB
fcis-985	83	7	5	5	NUM
fcis-985	83	8	.	.	PUNCT
fcis-985	84	1	conclusion	conclusion	NOUN
fcis-985	84	2	this	this	DET
fcis-985	84	3	article	article	NOUN
fcis-985	84	4	is	be	AUX
fcis-985	84	5	based	base	VERB
fcis-985	84	6	on	on	ADP
fcis-985	84	7	the	the	DET
fcis-985	84	8	python	python	NOUN
fcis-985	84	9	language	language	NOUN
fcis-985	84	10	,	,	PUNCT
fcis-985	84	11	learning	learn	VERB
fcis-985	84	12	200	200	NUM
fcis-985	84	13	photos	photo	NOUN
fcis-985	84	14	of	of	ADP
fcis-985	84	15	boys	boy	NOUN
fcis-985	84	16	and	and	CCONJ
fcis-985	84	17	200	200	NUM
fcis-985	84	18	photos	photo	NOUN
fcis-985	84	19	of	of	ADP
fcis-985	84	20	girls	girl	NOUN
fcis-985	84	21	.	.	PUNCT
fcis-985	85	1	using	use	VERB
fcis-985	85	2	a	a	DET
fcis-985	85	3	three	three	NUM
fcis-985	85	4	-	-	PUNCT
fcis-985	85	5	layer	layer	NOUN
fcis-985	85	6	neural	neural	ADJ
fcis-985	85	7	network	network	NOUN
fcis-985	85	8	,	,	PUNCT
fcis-985	85	9	an	an	DET
fcis-985	85	10	input	input	NOUN
fcis-985	85	11	layer	layer	NOUN
fcis-985	85	12	,	,	PUNCT
fcis-985	85	13	a	a	DET
fcis-985	85	14	hidden	hidden	ADJ
fcis-985	85	15	layer	layer	NOUN
fcis-985	85	16	and	and	CCONJ
fcis-985	85	17	an	an	DET
fcis-985	85	18	output	output	NOUN
fcis-985	85	19	layer	layer	NOUN
fcis-985	85	20	,	,	PUNCT
fcis-985	85	21	the	the	DET
fcis-985	85	22	detection	detection	NOUN
fcis-985	85	23	of	of	ADP
fcis-985	85	24	face	face	NOUN
fcis-985	85	25	and	and	CCONJ
fcis-985	85	26	gender	gender	NOUN
fcis-985	85	27	is	be	AUX
fcis-985	85	28	finally	finally	ADV
fcis-985	85	29	realized	realize	VERB
fcis-985	85	30	.	.	PUNCT
fcis-985	86	1	the	the	DET
fcis-985	86	2	experimental	experimental	ADJ
fcis-985	86	3	results	result	NOUN
fcis-985	86	4	also	also	ADV
fcis-985	86	5	show	show	VERB
fcis-985	86	6	that	that	SCONJ
fcis-985	86	7	the	the	DET
fcis-985	86	8	neural	neural	ADJ
fcis-985	86	9	network	network	NOUN
fcis-985	86	10	can	can	AUX
fcis-985	86	11	achieve	achieve	VERB
fcis-985	86	12	good	good	ADJ
fcis-985	86	13	results	result	NOUN
fcis-985	86	14	in	in	ADP
fcis-985	86	15	the	the	DET
fcis-985	86	16	fields	field	NOUN
fcis-985	86	17	of	of	ADP
fcis-985	86	18	face	face	NOUN
fcis-985	86	19	recognition	recognition	NOUN
fcis-985	86	20	and	and	CCONJ
fcis-985	86	21	gender	gender	NOUN
fcis-985	86	22	recognition	recognition	NOUN
fcis-985	86	23	.	.	PUNCT
fcis-985	87	1	the	the	DET
fcis-985	87	2	experiment	experiment	NOUN
fcis-985	87	3	shows	show	VERB
fcis-985	87	4	that	that	SCONJ
fcis-985	87	5	the	the	DET
fcis-985	87	6	gender	gender	NOUN
fcis-985	87	7	can	can	AUX
fcis-985	87	8	be	be	AUX
fcis-985	87	9	successfully	successfully	ADV
fcis-985	87	10	detected	detect	VERB
fcis-985	87	11	,	,	PUNCT
fcis-985	87	12	and	and	CCONJ
fcis-985	87	13	the	the	DET
fcis-985	87	14	experimental	experimental	ADJ
fcis-985	87	15	method	method	NOUN
fcis-985	87	16	in	in	ADP
fcis-985	87	17	this	this	DET
fcis-985	87	18	paper	paper	NOUN
fcis-985	87	19	is	be	AUX
fcis-985	87	20	feasible	feasible	ADJ
fcis-985	87	21	and	and	CCONJ
fcis-985	87	22	effective	effective	ADJ
fcis-985	87	23	.	.	PUNCT
fcis-985	88	1	references	reference	NOUN
fcis-985	88	2	[	[	X
fcis-985	88	3	1	1	NUM
fcis-985	88	4	]	]	SYM
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fcis-985	88	6	x	x	INTJ
fcis-985	88	7	.	.	PUNCT
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fcis-985	89	3	face	face	NOUN
fcis-985	89	4	recognition	recognition	NOUN
fcis-985	89	5	based	base	VERB
fcis-985	89	6	on	on	ADP
fcis-985	89	7	gabor	gabor	PROPN
fcis-985	89	8	wavelet	wavelet	PROPN
fcis-985	89	9	and	and	CCONJ
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fcis-985	89	12	.	.	PUNCT
fcis-985	90	1	ieee	ieee	NOUN
fcis-985	90	2	computer	computer	NOUN
fcis-985	90	3	society	society	NOUN
fcis-985	90	4	,	,	PUNCT
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fcis-985	91	1	[	[	X
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fcis-985	91	3	]	]	X
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fcis-985	91	5	x	x	X
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fcis-985	91	7	,	,	PUNCT
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fcis-985	91	9	l	l	PROPN
fcis-985	91	10	h	h	PROPN
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fcis-985	91	13	b	b	PROPN
fcis-985	91	14	,	,	PUNCT
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fcis-985	91	16	al	al	PROPN
fcis-985	91	17	.	.	PROPN
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fcis-985	91	19	of	of	ADP
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fcis-985	91	21	recognition	recognition	NOUN
fcis-985	91	22	in	in	ADP
fcis-985	91	23	remote	remote	ADJ
fcis-985	91	24	sensing	sensing	NOUN
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fcis-985	92	7	.	.	PUNCT
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fcis-985	93	2	3	3	X
fcis-985	93	3	]	]	X
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fcis-985	93	12	networks	network	NOUN
fcis-985	93	13	in	in	ADP
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fcis-985	93	15	image[j	image[j	PROPN
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fcis-985	93	17	.	.	PUNCT
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fcis-985	94	3	xi'an	xi'an	PROPN
fcis-985	94	4	university	university	PROPN
fcis-985	94	5	of	of	ADP
fcis-985	94	6	science	science	PROPN
fcis-985	94	7	&	&	CCONJ
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fcis-985	94	9	,	,	PUNCT
fcis-985	94	10	2005	2005	NUM
fcis-985	94	11	.	.	PUNCT
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fcis-985	95	3	]	]	X
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fcis-985	95	5	t	t	PROPN
fcis-985	95	6	,	,	PUNCT
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fcis-985	95	8	j	j	PROPN
fcis-985	95	9	,	,	PUNCT
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fcis-985	95	11	k	k	PROPN
fcis-985	95	12	,	,	PUNCT
fcis-985	95	13	et	et	NOUN
fcis-985	95	14	al.target	al.target	ADP
fcis-985	95	15	threat	threat	NOUN
fcis-985	95	16	assessment	assessment	NOUN
fcis-985	95	17	using	use	VERB
fcis-985	95	18	particle	particle	NOUN
fcis-985	95	19	swarm	swarm	NOUN
fcis-985	95	20	optimization	optimization	NOUN
fcis-985	95	21	and	and	CCONJ
fcis-985	95	22	bp	bp	PROPN
fcis-985	95	23	neural	neural	PROPN
fcis-985	95	24	network[c]//	network[c]//	PROPN
fcis-985	95	25	the	the	DET
fcis-985	95	26	2019	2019	NUM
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fcis-985	95	30	computing	computing	NOUN
fcis-985	95	31	and	and	CCONJ
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fcis-985	95	33	technologies	technology	NOUN
fcis-985	95	34	conference	conference	NOUN
fcis-985	95	35	.	.	PUNCT
fcis-985	96	1	2019	2019	NUM
fcis-985	96	2	.	.	PUNCT
fcis-985	97	1	[	[	X
fcis-985	97	2	5	5	X
fcis-985	97	3	]	]	X
fcis-985	97	4	antennas	antenna	NOUN
fcis-985	97	5	propagat	propagat	ADJ
fcis-985	97	6	.	.	PUNCT
fcis-985	98	1	,to	,to	PUNCT
fcis-985	98	2	be	be	AUX
fcis-985	98	3	published.a	published.a	NOUN
fcis-985	98	4	strategy	strategy	NOUN
fcis-985	98	5	for	for	ADP
fcis-985	98	6	making	make	VERB
fcis-985	98	7	lane	lane	NOUN
fcis-985	98	8	-	-	PUNCT
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fcis-985	98	15	risk	risk	NOUN
fcis-985	98	16	field	field	NOUN
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fcis-985	98	19	neural	neural	ADJ
fcis-985	98	20	network	network	NOUN
fcis-985	99	1	[	[	X
fcis-985	99	2	6	6	NUM
fcis-985	99	3	]	]	X
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fcis-985	99	5	j	j	PROPN
fcis-985	99	6	,	,	PUNCT
fcis-985	99	7	qin	qin	PROPN
fcis-985	100	1	d.the	d.the	DET
fcis-985	100	2	mutation	mutation	NOUN
fcis-985	100	3	seagull	seagull	NOUN
fcis-985	100	4	algorithm	algorithm	NOUN
fcis-985	100	5	optimizes	optimize	VERB
fcis-985	100	6	the	the	DET
fcis-985	100	7	speech	speech	NOUN
fcis-985	100	8	emotion	emotion	NOUN
fcis-985	100	9	recognition	recognition	NOUN
fcis-985	100	10	of	of	ADP
fcis-985	100	11	bp	bp	PROPN
fcis-985	100	12	neural	neural	PROPN
fcis-985	100	13	network[c]//	network[c]//	PROPN
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fcis-985	100	15	2021	2021	NUM
fcis-985	100	16	:	:	PUNCT
fcis-985	100	17	2021	2021	NUM
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fcis-985	100	21	on	on	ADP
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fcis-985	100	23	and	and	CCONJ
fcis-985	100	24	broadband	broadband	NOUN
fcis-985	100	25	networking	networking	NOUN
fcis-985	100	26	.	.	PUNCT
fcis-985	101	1	2021	2021	NUM
fcis-985	101	2	.	.	PUNCT