Measurement	of Very Large Scale

Biometric Systems: India's UID Project	

Srikanth Nadhamuni

Head of Technology,

UID Authority of India	

srikanth@egovernments.org
UID IntroducHon
	 	 	 	 	 	

	 	 	 	 	 	 		

	 	 	 	

	 	 	 	 	

	 	 	

	 	

• Provide a	Unique number to every resident	of India	

– Earlier databases plagued by duplicates and errors

– remove ghost	idenHHes

• To improve government	service delivery

• Provide ubiquitous verifiable idenHty proof

– Deliver govt. subsidies

– banking

– access control

	 	

	 	 	 	

	 	

	 	

	 	 	 	

• MulH-ABIS system

• Dynamic AllocaHon to ABIS

• Biometric Accuracy

– Absolute/RelaHve methods

• Accuracy as gallery grows
ENROLLMENT
Enrollment	Process
Enrolment	Accuracy Study		 based on a	sample size of 84million
IMAGE QUALITY
	 	 	 	

	 	 	 	

	 	 	 	 	 	 	 	 	

	 	 	 	 	 	 	 	 	 	

	 	 	 		 	 	 	

• Methodology

– Quality metrics embedded in enrollment	packet	

– Face: ICAO-- (slightly relaxed)

– FP: Poor quality when there is at	least	one finger with

NFIQ >3 in each of three slaps (4, 4, 2)

– Iris: Poor quality when “Irisness” score <	50

(proprietary)
		 	 	 	 	 		

	 	

	 	 	 	 	 	 	

	 	 	 	 	

	 		

• Govt. Policy - everyone must	be enrolled

– ie FTE=0%

• Biometric FTE: 0.14%	(no FP & Iris captured)

• Poor Quality FP & Iris: 0.23%	

• Poor Quality

– FP: 2.9%,	

– Iris: 3.0%	
	 	 	

	 	 	 	 	 	 	

	 	 		 	

	 	 	 	 	 	

• Face Quality: 85%

• Iris (L & R): 93.4% & 92.5%

• Fingerprint	

– Slap: 93.4% (L), 93.6%(R)

– Thumbs:	91.6%

• Quality improves over Hme (learning curve)
	 	 	 	 	 	 	 	

	 	 	 	 	 	

	 	 	 	

	 	 	 	 	 	 	

	 	 	 	

	 	 	 	 	 	

• MulHple modality improves FTE by 10 to 25x

• Quality is comparable to Western results

– Diverse demographic

– Effect	of manual labor (FP)

• Good biometric obtainable from 5 yrs age

• Senior populaHon difficult	but	sHll feasible

• Considering age specific algorithms –	for Auth
IDENTIFICATION		
	 	 	 	 	 	

	 	 	 	 	

• Absolute	

– TradiHonal method - Ground truth is pre-determined

• RelaHve

– MulH-Algorithm comparison and manual review

99.943% 

	 	

	 	 	 	 	 	 	 	 	 	

	 	 	

	 	 	 	 	

	 		 	 	

 

• FPIR	

– Probe size: 4M	

– False rejects: 2,309 (eg. A1 –	HIT, A2 –	NO-HIT, A3 –	NO-HIT)

• FNIR	

– Probe	size:	32,000	

– False accept: 11

• FPIR: 0.057%	 @ Gallery = 84 Million • FNIR: 0.035%	

• NIST 7112 Ten FP Results

– FPIR: 0.035% @	Gallery=	1 Million
	 	 	

	 	 	 	

FNIR: 0.0004% 	 	 	 	 	 	 	 	 	 	 	 	

	 	 	 	 	

• Gallery: 130 Million

• 21,000 Demographically verified duplicates as

probes	

• 

Note: These duplicates seemed to have beper image quality to overall

populaHon

Rela%ve method produces comparable results
	 	 	 	 	 	

	 	

	 	 	 	

	 	 	

	 	 	 	 	 	

• 40%	of	suspected	duplicates	

• Operator using their biometrics to help

residents

• Mixed

– Different	persons in different	apempts

– New process eliminates them

• Anomalous

– Different	persons in an apempt	

– ProblemaHc for ABIS doing sequenHal fusion
	 	 	 	 	 	

	 	 	 	 	 	 	 	

	 	 	 	 	 	

	 	 	 	

• 10 to 100X	 improvement	through 2 modaliHes

• CompeHHve advantage of using 3 ABIS & SDKs

• ConHnuous FPIR/FNIR	measurements

• Possible to maintain low FPIR/FNIR	over wide

range of gallery size
• Enrollment:

– hpp://uidai.gov.in/images/FrontPageUpdates/

role_of_biometric_technology_in_aadhaar_jan21_2012.pdf

– hpp://uidai.gov.in/UID_PDF/Front_Page_ArHcles/Documents/PublicaHons/

Aadhaar_ABIS_API.pdf

– hpp://uidai.gov.in/images/FrontPageUpdates/uid_enrolment_poc_report.pdf

• VerificaHon:

– hpp://uidai.gov.in/images/FrontPageUpdates/

role_of_biometric_technology_in_aadhaar_authenHcaHon.pdf

– hpp://uidai.gov.in/images/FrontPageUpdates/

aadhaar_authenHcaHon_api_1_5_rev2.pdf

– hpp://stqc.gov.in/sites/upload_files/stqc/files/STQC%20UIDAI

%20BDCS-03-08%20UIDAI%20Biometric%20Device%20SpecificaHons

%20_AuthenHcaHon_1.pdf

• UIDAI	Documents

hpp://uidai.gov.in/uidai-documents.html
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