Fingerprints: Recognizing 75 Billion Patternsbiometrics.cse.msu.edu/Presentations/AnilJain... ·...

Anil Jain

Michigan State University

October 23, 2018

http://biometrics.cse.msu.edu/

Fingerprints:

75 Billion-Class Recognition Problem

Cumins and Midlo, Finger Prints, Palms and Soles, Dover, 1961

Friction Ridge Patterns

2

Dermatoglyphics. Derma: skin; Glyphs: carving

3

Fingerprints

Each finger, including those of identical twins, has a different pattern

0.73 x 0.84 in.

75-Class Recognition Problem

• 7.5 billion x 10 = ~75 billion pattern classes

• 350 K births/day; no. of classes keeps increasing

4

First Encounter with Fingerprints

Ratha, Rover, Jain, "An FPGA-Based Point pattern Matching Processor with application to Fingerprint Matching", CAMP `95,

Sun SPARCstation host

~100 MHz CPU; 512 MB RAM

• Fingerprint matching on Sun SPARC: 70 matches/sec

• 100-times speedup on Splash 2 FPGA @1 MHz clock

16 Xilinx 4010s as PEs (512 KB memory)

5

Fingerprint Formation• Ridge formation starts at 1 or 2 focal points and spreads

over the fingertip

• Localized ridge units merge to form ridges at ~10.5

weeks of gestational age

• Fingerprints possess genotype & phenotype properties

L. S. Penrose and P. T. Ohara. The development of the epidermal ridges. Journal of Medical Genetics. 1973

M. Okajima. Development of dermal ridges in the fetus. Journal of Medical Genetics. 1975 6

Fingerprint Milestones

300

B.C.

1839 1869 1883 1900 1905 1924 1972 19991963 2001 2003 2008 2013 2014 20171858 2018

Fingerprint as a personal mark

A Chinese deed of sale with a fingerprint

Early use of fingerprints for Civil applications

(Bengal, India)

Habitual Criminals Act“What is wanted is a means ofclassifying the records ofhabitual criminal, such that assoon as the particulars of thepersonality of any prisoner(whether description,measurements, marks, orphotographs) are received, itmay be possible to ascertainreadily, and with certainty,whether his case is in theregister, and if so, who he is”

First use of fingerprints in

British criminal case

Bertillonage invented

Galton / Henry fingerprint system adopted by Scotland Yard

• Seventeen classes

• Whorl (double loop), loop (left and right)

& arch cover 99% of fingerprints

Delta Core

US Congress authorizes DOJ to collect fingerprints and arrest information

Identimat: First commercial use of biometrics

Trauring publishes paper on fingerprint matching in Nature Goldstein et al. publish face recognition paper in Proc. IEEE (1971)

FBI inaugurates full operation

of “IAFIS”

State AFIS

State AFIS

State AFIS

State AFIS IAFIS

Forensics Other operations

Criminal booking

9/11 terrorist attacks lead to govt. mandates to use biometrics in

regulating intl. travel

US-VISIT TouchIDFaceID

Apple Pay

Supreme Court upholds the constitutional

validity of Aadhaar

“Aadhaar gives dignity to the marginalized. Dignity

to the marginalized outweighs privacy,” Justice

Sikri

Aadhaar

FBI Next Generation Identification

7

Drivers: Lack of Trust

•Osmania University (OU) enhanced the

exam fee in all the affiliated colleges by

Rs. 100 per semester for

implementation of biometric attendance

system. Times of India, Oct 22, 2018

• No end to JNTU-H fake certification

verifications. HANS INDIA, Times of India, Oct 12, 2018

8

Enablers: Fingerprint Readers

1892Juan VucetichInk and Paper

1990Optical sensor

1990Capacitive sensor

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Enablers: Processors, RAM, Algorithms

Courtesy: James Blanchard, Michigan State Police

1960s

1989

• 725K tenprints

• 15K matches/sec

2017• 4M tenprints

• 1M matches/sec

Ridge

Voting

Fingerprint Enhancement

Hong, Wang and Jain, IEEE Trans. PAMI, 1999

Fingerprint Representation

Level-1 Level-2 Level-3 Minutiae Pores and incipient ridges

Ending

Bifurcation

Orientation Field

Singular Points

Deltas

Cores Pores

Incipient Ridge

Ridge flow and pattern type

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Template: A compact representation of fingerprint features

Minutiae Extraction

Input Image

Ridge Thinning

Minutiae Detection

Ridge Flow Ridge Filter

PostprocessingExtracted Minutiae

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Minutiae Descriptors

•Ridge Flow-based Descriptor

• Ridge flow values in the minutiae neighborhood

•Neighboring minutiae-based Descriptor

• Set of minutiae in a local neighborhood

Flow-based Descriptor Minutiae-based DescriptorMinutia Neighborhood

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Enrolled fingerprint

Fingerprint Comparison

Similarity = 0.9Query fingerprint

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How to Align?

Gallery Fingerprint

Query Fingerprint

16

Jain, et al. An Identity Authentication System Using Fingerprints, Proc. IEEE, 1997

Freq

uen

cy

Similarity score

Recognition Performance

ROC curve

1

1

17

Threshold determines tradeoff between FAR & FRR

System Requirements

100K visitors/day to Disney Park, Orlando18

• Usability

• Fast verification to maintain throughput

• Low error rates, especially FRR

• Day/night operation

• Robust to finger condition: wet, dry,..

• Return on investment

• Embedded system

• Template encryption

Aadhaar:

World’s Largest Biometric System

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121 crore (1.21 billion) individuals have been enrolled

De-duplication:

Limited Capacity of Fingerprints

Aadhaar Authentication

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Daily Authentication Transactions

“100% successful authentication NOT possible,” UIDAI CEO admits in SC

https://uidai.gov.in/aadhaar_dashboard/auth_trend.php

State of the Art Performance

• Authentication: TAR of 99.9% @FAR = 0.001%

• Retrieval (search)• Plain prints: 99.3% (100K background gallery)

• Latent prints: 67.2% (70.2% with image + markup)

C. Watson, et al.. Fingerprint Vendor Technology Evaluation, NISTIR, 2012

M. Indovina, et al.. ELFT-EFS Evaluation of Latent Fingerprint Technologies: Extended Feature Sets NISTIR, 2012

Rolled prints Plain prints Latent prints

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Sources of Error

No. of false minutiae = 27No. of false minutiae = 7No. of false minutiae = 0

24

489 368 6 329 77

29 11 12 21 20

Query

Sources of ErrorGenuine comparisons

Imposter comparisons

Intra-finger variations and Inter-finger similarity

Accuracy

Scale

Usability

Unusable

Hard to Use

Easy to Use

Transparent to User

101

103

105

107

90%

99%

99.99%

99.999%

What’s Next?

Fingerprint Recognition is not solved!

Scalability

• Assume one billion users

• Identification Performance

• False Negative Identification Rate (FNIR): user is

enrolled, but not retrieved

• False Positive Identification Rate (FPIR): user is not

enrolled, but an identity is returned

• Identification & verification performances are related

FPIR = 1 – (1 – FMR)N ; FNIR = FNMR

• Suppose for N = 109 enrollment, we require

FNIR = 0.0001%; FPIR = 0.001%

Require a matcher: FMR ≈ 10-12 %; FNMR = 0.0001%

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Capacity & Persistence

• Uniqueness: How many different

individuals can we recognize?

• Permanence: Does the recognition

performance change over time?

6-digit code:106 unique PINs

PINs do not become “stale”

Prob. of False Correspondence• "Two like fingerprints would be found only once every

1048 years” (Sc. Am, 1911)

• Prob. of a fingerprint with n minutiae and another with

m minutiae sharing q minutiae

(a) M=52

m=n=q=26

P = 2.40 x 10-30

(b) M=52

m=n=26, q=10

P = 5.49 x 10-4

M = A/C

Pankanti, Prabhakar and Jain, “On the Individuality of Fingeprints”, IEEE PAMI, 2002

29

Persistence

Yoon and Jain, "Longitudinal Study of Fingerprint Recognition", PNAS, 2015

• Database: fingerprints of 20K convicts with an

average of 8 arrests over a span of 12 years

• Longitudinal model showed: Fingerprint accuracy (i)

is stable over 12 years, (ii) accuracy depends more

on fingerprint quality than time gap

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Spoof Attacks

Chugh, Kai and Jain, "Fingerprint Spoof Buster: Use of Minutiae-centered Patches", IEEE TIFS, 2018

Requirements: TAR = 98% @FAR = 0.2%

Fingerprint Obfuscation

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Fingerprint of Gus Winkler (1933) before and after alteration

Template Protection

33

Original Fingerprint ImageISO Fingerprint TemplateReconstructed Fingerprint ImageSimilarity Score = 460 (VeriFinger)

Fingermarks (Latent Prints)

34

Madrid Train Bombing (2004)

Partial print on a duffel bag Brandon Mayfield’s prints in file

35

Automated Latent AFIS

Acquisition Cropping Enhancement Minutiae Comparison

Reference database

Feedback

290 71 70 48

Candidate list 36

Kai and Jain, IEEE PAMI, 2018

Successful MatchLatent

EnhancedLatent

Mated Rolled

MatedRolled

# Matched minutiae = 13

Similarity score = 38

# Matched minutiae = 2

Similarity score = 3

Infant Fingerprints

Digital Persona U.are.U (500 ppi) MSU Match in Box (1900 ppi) Custom NEC Reader (1270 ppi)

Jain, Arora, Cao, Best-Rowden, Bhatnagar, Fingerprint recognition of young children, IEEE TIFS, 2017

Engelsma, Cao, Jain, Fingerprint Match in Box. IEEE BTAS, 2018

Engelsma, Cao, Jain, Fingerprint Match in Box. IEEE PAMI, 2018

Right thumb of a 3 month old infant captured with 500,1270 & 1900 ppi readers

Fingerprint Match in Box

(a) (b) (c)

A low cost ($200), open source, 1900 ppi compact (10 cm cube) fingerprint reader with embedded spoof

detector, feature extractor, and matcher with 1:100K search; thumbprint of 3-month-old baby

Privacy Concerns

40

Security v. Privacy

Summary

• Fingerprint based transactions are used by

hundreds of millions of citizens worldwide

• Applications: mobile phone unlock, social

benefits disbursement, border crossing,

forensics; new applications are emerging

• Challenges: sensor design, image quality,

robust & accurate solution, privacy, security

• It’s all about lack of TRUST

Security v. Privacy

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