The	push	towards	zero	error	biometrics	

NIST	Forensic	Symposium	

Elham	Tabassi	

Image	Group	

Informa:on	Access	Division	

Informa:on	Technology	Laboratory	

Friday,	November	30,	12	
Open	set	1:N	Search	
1:N	Latent	Search	

Latent	image	

Features	

Search	Template	

Candidate	List	

Bob 1278 		

Alice 357

Mike 267	

Lisa 245	

MaQ 209	

FPIR	

	(False	Alarm	Rate)	

FNIR	(Miss	Rate)	
ACE-V	

Analysis	

Value	

Determina:on	

(VID,	VOE,	NV)	

Feature	

Markup	(EFS)	

1:N	search	

(AFIS)	

Comparison	

Candidate	Pairs	

Level-1	detail	

Level-2	detail	

Level-3	detail	

Individualiza:on	

Evalua:on	

Exclusion	

Inconclusive	

Verifica:on	

Analysis	

Comparison	

Evalua:on	

Validate	or	

Reject	the	

conclusion	

		

Quality	of	latent,	Quality	of	the	exemplar	and	the	size	of	the	overlap	area	between	

the	two	play	significant	role	in	accuracy	and	reliability	of	the	conclusion.		

If	the	exemplar	is	not	of	sufficient	quality,	the	conclusion	should	be	Inconclusive.	
Need	for	numerical	evalua:on	of	quality	

≫ To	supplement	the	fingerprint	examina:on	process	by	one	that	has	a	

sta:s:cal	model,	supported	by	appropriate	databases	for	calcula:ng	

numerical	measures	of	weight	of	evidence	

– Perhaps	based	on	automa:c	latent	recogni:on	algorithms		

• Some	of	VEO	or	NV	were	successfully	iden:fied	by		latent	comparison	algorithm.	(NIST	

ELFT-	EFTS).	

≫ Resolve	the	variability	among	the	examiner’s	value	determina:on	

– For	356	latents,	unanimous	value	determina:on	achieved	43%.			

– 85%	of	NV	decisions	and	93%	of	VID	decisions	were	repeated	by	the	same	

examiner	ader	a	:me	gap	while	only	55%	of	VEO	decisions	were	repeated.	

(Ulery	et.	al	–	PNAS	2011).	

≫ While	acknowledging	the	overall	reliability	of	the	conclusions	of	majority	

of	fingerprint	comparisons	performed	over	the	past	century,	and	their	

contribu:on	to	the	criminal	jus:ce	system
Lights-out	Latent	search	

Lights	out		

Iden:fica:on	

Examiner	Adjudica:on		

of	search	results		

Quality	assessment	for		

error	suppression	

≫ Quan:fy	informa:on	content	of	

latent	

– Suitable	for	automa:c	feature	

markup	or	manual?	

≫ Quan:fy	quality	of	the	reference	

print

≫ Reliability	of	latent	to	reference	

comparison	

– Sufficient	overlap	area?		

Nov30 - The 177th birthday of Mark Twain 
Quality	assessment	for	error	suppression	

Reference	print	

≫ Quan:ta:ve	assessment	of	u:lity	

of	the	fric:on	ridge	print.	

– Is	this	suitable	for	matching?	

– NFIQ,	others.	

≫ By	numbers:			

– 34.5%	of	opera:onal	IAFIS	

exemplars	are	of	low	quality	

(NFIQ	3,4,5).		

					(Ulery,	et.	Al	PNAS	2011).		

Crime	scene	Latent	print	

≫ Quan:ta:ve	assessment	of	informa:on	

content	of	the	latent.	

≫ Latents	recovered	from	crime	scenes	are	

oden	limited	in	size,	of	poor	quality,	

distorted	and	affected	by	interference	from	

the	substrate.		

≫ A	growing	body	of	literature	ques:ons	

scien:fic	founda:on	and	transparency	of	

the	evalua:on	of	the	weight	of	evidence	

associated	with	any	par:cular	fingerprint	

comparison		

– Zabell	(2005);	Office	of	the	US	Inspector	

General	(2006);	Saks	and	Koehler	(2005,	

2008);	Na:onal	Research	Council	of	the	

Na:onal	Academies	(2009)	

≫ Recent	related	work	

– On	latent	fingerprint	Quality	Yoon,	Liu,	Jain,	

2012	

– Quan:fying	the	weight	of	evidence	from	a	

forensic	finger-	print	comparison:	a	new	

paradigm,	C.	Neumann,	2011	
	What	is	`quality’?	

`standard’	defini>on	

quality

the	degree	to	which	a	biometric	sample	fulfils	

specified	requirements	for	a	targeted	applica:on

NOTE:	Specified	quality	requirements	may	address	

aspects	of	quality	such	as	focus,	resolu:on,	etc.		

Implicit	quality	requirements	address	the	likelihood	

of	achieving	a	correct	matching	result.	

quality	score

a	quan:ta:ve	expression	of	quality	

u>lity

the	observed	performance	of	a	biometric	sample	or	

set	of	samples	in	one	or	more	biometric	systems		

NOTE:	The	character	of	the	sample	source	and	the	

fidelity	of	the	processed	samples	contribute	to—or	

similarly	detract	from—the	u:lity	of	the	sample

NOTE:	U:lity	may	combine	performance	measures	

such	as	FMR,	FNMR,	failure	to	enrol	rate,	and	

failure	to	acquire	rate

Predic>ve	of	performance	

Black	Box	

Quality	

Assessment		

93	

26	

A	biometric	quality	assessment	method	derives	a	

numerical	quality	value	from	an	input	biometric	

sample.	The	quality	value	is	related	to	the	

biometric	error	rates	that	are	likely	to	be	realized	

when	the	sample	is	matched.		
≫ NIST	developed	NFIQ	in	2004	

– Open	source,	publicly	available	

≫ Has	become	the	de-facto	standard	

≫ Key	innova:on:	quality	as	a	rank	sta:s:c	for	

performance	

≫ NFIQ	is	a	machine	learning	algorithm		

– Exploratory	variables:	image	proper:es	(minu:ae,	

ridge	clarity)	

– Response	variable:	separa:on	of	genuine	and	

impostor	comparison	

9	

NIST	fingerprint	image	quality	(NFIQ	1.0)	
quality		 NFIQ number	

NIST	Fingerprint	Image	Quality		

≫ feature	extrac>on:	computes	appropriate	signal	or	image	fidelity	

characteris:cs	and	results	in	an	11-dimensional	feature	vector.	

≫ neural	network:classifies	feature	vectors	into	five	classes	of	quality	

based	on	various	quan:les	of	the	normalized	match	score	

distribu:on.	

≫ quality	number:	an	integer	value	between	1(highest)	and	5	(poorest).

 feature 					neural		

extrac>on 				network
quality 

number NFIQ1.0

NIST	Fingerprint	Image	Quality	

=15

NFIQ’s	5	levels	of	quality	are		intended	to	be	predic:ve	of	the	rela:ve	

performance	of	a	minu:a	based	fingerprint	matching	system.		

NFIQ=1	indicates	high	quality	samples,	so	lower	FRR	and/or	FAR	is	

expected.	

NFIQ=5	indicates	poor	quality	samples,	so	higher	FRR	and/or	FAR	is	

expected.
gray	scale		

fingerprint	

image	

1	total	#	of	minu:a	

	2	#of	min.	with	q>.5			

	3	#of	min.	with	q>.6	

	4	#of	min.	with	q>.7	

	5	#of	min.	with	q>.8	

	6	#of	min.	with	q>.9

assessment	of	

	clarity	of	ridges			

and	valleys	

7 size	of	foreground						

8 quality	zone	1	

9 quality	zone	2						

10	quality	zone	3					

11		quality	zone	4	

NFIQ	–	feature	vector	

NIST	Minu:ae	detector	(mindtct	of	NBIS	distribu:on)	

has	been	used	for	feature	extrac:on.	
NFIQ	1.0	–	training	

11dimen:onal		

feature	vector		 neural network 

quality	number	

{1,2,3,4,5}	

1	is	the	best	and		

5	is	the	poorest	

training:	3900	

images	of	flat	

index	fingers	and	

thumbs	

						

A	full	similarity	

matrix	of	the	

training	set	is	

needed	to	

compute	the	

output	class	of	

neuralnetwork.	
Separa:on	of	genuine	and	impostor	distribu:on	
NFIQ=3

NFIQ=1

NFIQ=2

NFIQ=4

NFIQ=5

ALL

NFIQ	–	rank	sta:s:c	for	performance	

15	
NFIQ	1.0	–	test	of	:me	

+	

≫ Novel	defini:on	of	

biometric	quality		

– performance	related	

– accepted	by	the	community	

≫ Interoperability		

– uniform	interpreta:on	

– tuned	to	a	class	of	matcher		

≫ Open	source	

≫ Extensively	examined	

– by	NIST	and	others	

– tools	for	quality	

summariza:on,	slap,	...	

_	

≫ Aging			

– 	recogni:on	technology	

has	advanced	since	2004.	

≫ Efficiency	

– ~300	msec	per	image	-	not	

fast	enough	for	real	:me	

– takes	4	:mes	for	4-finger	

slap	

≫ Not	enough	levels	

– S:ll	sta:s:cally	significant	

≫ Insensi:ve	to	par:al	

prints	

		
NFIQ	2.0	

hQp://www.nist.gov/itl/iad/ig/development_nfiq_2.cfm	

Modular	Architecture	

Ac:onable	feedback	

0.00 0.02 0.04 0.06 0.08 0.10

0.07 0.08 0.09 0.10 0.11

fraction of genuine comparisons rejected 

 Comparator 1T − Dataset poebva − Finger 02

false non−match rate

ocl rvu32 rps mmb gab msb rvu16 fda lcs gsh of

`Standardized’	features	(ISO/IEC	29794-4)	

Tools	

Flags	to	iden:fy:	

− Fingerness

− Completeness	

− Wrong	Phalanges	

− Centerness		

− Low/high	pressure		

− Alteredness

hQp://biometrics.nist.gov/cs_links/quality/NFIQ_2/

NFIQ-2	Quality_Feature_Defin-Ver05.pdf

NFIQ	Lite	

− Fast	for	Mobile	applica:ons	

− Use	of	innova:ve	methods	such	as	

self	organizing	map	

Mapping	curves	

− With	NFIQ	1.0	

− Calibra:on	curve	for	each	NFIQ	2.0	

comparator	par:cipants.	
Uses	of	quality	assessment	

Subject	

presenta:on	

•Improper	

presenta:on	

detec:on	

•Presenta:on	

aQack	detec:on	

Acquisi:on	device	

•Hardware	built-in.	

Quality	in	capture	

loop.	

•`peak’	imaging	

capability	

•No	control	on	FTA	

-		Hard	to	tweak	to	

certain	

applica:ons	

Beyond	scanner	

• Automated	(e.g.,	

NFIQ)	or	visual	by	

human	

• Automated	at	

client-side	or	

backend		

• Ac:onable	feedback	

for	re-capture	

Operator	review	

• Par:cularly	for	high	

value	images	

• It	is	expensive	

• Requires	training	

of	operators	+	

takes	:me	

Allows	for		

− Adopting	threshold	for	specific	scenario			

− Monitoring	Seasonal	variations,	atypical	

collection	site/queue/device,	etc.	
Quality	in	large	scale	deployments	

Na>onal	

≫ DHS	US-VISIT	

≫ Low	enforcement		

– FBI	CJIS	

≫ DoD

Interna>onal	

≫ Unique	Iden:fica:on	

Authority	of	India		

≫ EU-VIS	

≫ Law	enforcement	

(Germany	BKA)	

≫ E-passport	
Available	Quality	Assessment	Algorithms	

NIST	Open	source	implementa>on	

≫ 		Finger	

– NFIQ	(circa	2004)	

• www.nist.gov/itl/iad/ig/nbis.cfm

• NIST	IR	7151	

– Next	Genera:on	NFIQ	(NFIQ	2.0)	

underway	

• www.nist.gov/itl/iad/ig/

development_nfiq_2.cfm			

– Latent	

≫ 			Iris	

– Not	yet,	but	in	near	future	

• NIST	Iris	Image	Quality	(NIIQ)	

• Some	methods	are	documented	in	

technical	literature	

≫ 			Face	

– Not	yet	–	no	plans	yet.	

Proprietary	implementa>on	

≫ Needs	tes:ng	

– Their	effec:veness	in	

predic:ng	performance	

have	to	evaluated.	

≫ Need	calibra:on	

– To	interpret	scores	

– To	achieve	

interoperability	

≫ Issues	with	vendor	lockin	

Academia?	Others?	
Calibra:on	

Rejec:ng	comparisons	which		

min(nfiq-sample1,nfiq_sample2)	=	5	

Rejec:ng	comparisons	which		

min(nfiq-sample1,nfiq_sample2)	=	4	

Rejec:ng	comparisons	which		

min(nfiq-sample1,nfiq_sample2)	=	3	

Rejec:ng	comparisons	which		

min(nfiq-sample1,nfiq_sample2)	=	2	
Challenges	

≫ Pairwise	Q	

≫ Computa:on	:me	

≫ Different	matcher	–	different	sensi:vi:es	

≫ Limita:on	on	available	data,	par:cularly	data	

with	know	degree	of	specific	impairments	
WE,	AT	NIST	
Focus	and	Impact	

NIST	Biometric	Quality	Program	

Push	Towards	Zero	Error	Biometrics	

Strengthening	

Science	

Failure	

Analysis	

	Iden:fying	the	

likely	causes	of	

recogni:on	

error,		

quan:fying	

their	effect	

and		ways	to	

mi:gate	them.	

Research	

NIST	IR	7155	

ICIP	2005	

NIST	IR	7820	

Advancing	

metrology	

Performance	

Evalua:on	

Quan:ta:ve	

means	of	

assessing	

performance	

of	quality	

assessment	

algorithms	

(IREX	II	IQCE)	

Evalua:on	

NIST	IR	7820	

PAMI	2007	

ICPR	2010	

Developing	

Standards		

Requirements	

Specifica:ons	

On	image	

proper:es	

affec:ng		

performance,	

and	on	capture	

device	

Standard	

ISO/IEC	29794	

ISO/IEC	19794	

Developing	

Tool	Box	

Open	source	

Public	domain	

Reference	

implementa:o

ns	of	quality	

assessment	

algorithm,	iris	

segmenta:on	

Sodware	

NFIQ	1.0	

NFIQ	2.0	

NIIQ	1.0	

Best	Prac>ce	

Guidance		

Instruc:onal		+	

Guidance	

Materials	for	

quality	score	

summariza:on

+	Best	capture	

prac:ce	+	

example	

images	of	

various	quality		

Report	

NIST	IR	7422	

NIST	IR	8XXX	

Enumera>ve	

Bibliography			

Technical	

Literature		

Reports,	white	

papers,	

publica:ons	

relevant	to	

biometric	

quality	and	iris	

image	quality	

in	par:cular	

Webpage	

www.nist.gov/

itl/iad/ig/

bio_quality.cf

m

Coordina>on+	

Collabora>ons	

Workshops,		

Conferences	

Grants	(WVU,	

NYU	Poly)	

BQW	2006,	07	

IBPC	2010,	12	

NFIQ	2010,12	
Thanks		

Sponsors	 Collaborators	+	NFIQ	2.0	partners	
Contact	Info:	

Elham	Tabassi

tabassi	AT	nist	DOT	gov

301	975	5292