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IBM Research

© 2002 IBM Corporation

Research Challenges in Biometrics

Nalini K. Ratha Ph. D.Research Staff MemberExploratory Computer Vision GroupIBM T.J Watson Research CenterHawthorne, NYratha@us.ibm.com

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IBM Research

© 2002 IBM Corporation

What is Computer Vision?

� Greater understanding of human vision and the brain� Various deep and attractive scientific mysteries

what can we know from an image?

how does object recognition work?� Thanks to Digital cameras and camcorders – Images and video are everywhere� Several interesting and useful applications involving building representations of the 3D world

from pictures– medical imaging,

– household robots,

– security,

– Automobile navigation,

– face finding, …

Computer vision is the science and technology of machines that see. As a scientific discipline, computer vision is concerned with the theory for building artificial systems that obtain information from images. The image data can take many forms, such as a video sequence, views from multiple cameras, or multi-dimensional data from a medical scanner.

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Computer Vision

VeggieVision Video Management Audio-Visual Speech Reco

PersonVehicle

(Exiting-Lot)

Group

(Walking)

Video Surveillance Biometrics

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IBM Research

© 2002 IBM Corporation

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Outline

� Introduction� Biometrics system architecture� Research Challenges� Enrollment issues� Multi-biometrics� Large scale Identification� Performance evaluation� Biometrics and Security� Attack models� Conclusions

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IBM Research

© 2002 IBM Corporation

Authentication technologies

Method Examples Comments

What you know User ID, password, PIN

ForgottenShared Many passwords are easy to guess

What you have Credit cards, badges, keys

Lost or stolenSharedCan be duplicated

What you know and what you have ATM + PIN

PIN is a weak linkWriting PIN on cardShared

What you are Fingerprint, face.....NonrepudiableCannot be lostCannot be forgotten

Possession

Biometrics

and knowledge

Knowledge

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IBM Research

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Biometrics definitions (Wikipedia)

� Biometrics (ancient Greek: bios life, metron measure) refers to two very different fields of study and application. The first, which is the older and is used in biological studies, including forestry, is the collection, synthesis, analysis and management of quantitative dataon biological communities such as forests. Biometrics in reference to biological sciences has been studied and applied for several generations and is somewhat simply viewed as "biological statistics.“

� More recently and incongruently, the term's meaning has been broadened to include the study of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits.

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IBM Research

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Biometrics is a multi-faceted technology

Biometrics

Multimedia

InformationSecurity

Image/signalProcessing

Machine Learning

PatternRecognition

CMOSSensors

ComputerVision

Optics

Databases

StatisticalAnalysis

Lots of exciting opportunities at the intersections

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IBM Research

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Biometrics System Architecture

Feature extraction

Technology

Matcher Technology

SignalAcquisitionTechnology

1:1

1:N

App li ca tio n s

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IBM Research

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Top six biometrics

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IBM Research

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Identification vs. Verification

ID/Name

Yes/No

1 : 1 Match

I am who I say I am

ID

Who am I?

MatchMatch

1 : N Match

Biometricdatabase

Biometricdatabase

Central/distributed

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IBM Research

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Emerging biometrics

� Vein scan� Facial thermograph� DNA matching� Odor sensing� Skin pattern recognition� Gait recognition� Ear shape recognition� Palm recognition

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IBM Research

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Seven myths about biometrics

1. Biometric X is the "best" biometrics for all applications

2. Biometric X is unique for each individual

3. Large templates mean better accuracy

4. Our biometrics system is "plug and play" - requires no tuning

5. Real accuracy performance is predictable

6. The vendor reporting best FAR and FRR is the "most accurate" system

7. Multiple biometrics outperforms a single biometrics

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IBM Research

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Recent Special Issues� Nov. 2007: Proceedings of IEEE, Spl. Isuue on Biometrics

� April 2007: IEEE Trans. on Pattern Analysis and Machine Intelligence (T-PAMI), Spl. Issue on Biometrics

� Sept. 2007: IEEE Trans. on Information Forensics & Security (T-IFS), Spl. Issue on Biometrics

� Sept. 2007: IEEE Trans. on Systems, Man and Cybernetics Part B (T-SMC-B), Spl. Issue on Biometrics

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IBM Research

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Conference in this area

� IEEE CVPR Workshop on Biometrics� IEEE Biometrics Theory, Application and Systems (BTAS)� IEEE CVPR� IEEE ICIP� IEEE ICASSP� ICPR� ICB� SPIE Conf. on Biometrics

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IBM Research

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Leading journals in Biometrics

� IEEE Trans. on Pattern Analysis and Machine Intelligence� IEEE Trans. on Information Forensics and Security� IEEE Trans. on System, Man and Cybernetics Part A, Part B� IEEE Trans. on Image Processing� Pattern Recognition� Pattern Recognition Letters

� IEEE Biometrics Compendium to be launched soon

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IBM Research

© 2002 IBM Corporation

IEEE Certified Biometrics Professional

� The IEEE CBP program has officially launched and the program website is live!

� You can view the website by visiting – www.IEEEBiometricsCertification.org

� The two main components of the program:

– Certification: IEEE Certified Biometrics Professional™ (CBP) Exam

– Training: IEEE Certified Biometrics Professional™ (CBP) Learning System

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Recent New Books

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IBM Research

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Biometrics Research Agenda

� Template aging

� Fusion

� Modeling and scaling

� Quality of Biometrics data

� System Performance

� Interoperability

� Security of biometric applications and systems

NSF Research Agenda 2003

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IBM Research

© 2002 IBM Corporation

The Biometrics Challenge 2006 (www.biometrics.gov)

� Biometric Sensors

– – Rapid collection of face, finger and iris in mobile and harsh environments while still meeting technical, safety and quality standards

– Cameras and sensors that dynamically adjust to changing circumstances

– Quality collection of biometric data of non-cooperative users at distances

– Quality collection of biometrics data in relaxed conditions

– Next generation and Multi-biometric sensors

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Current limitations

� Inefficient and ineffective large-scale biometrics systems (lack one or more of the following characteristics)

– High recognition accuracy

– Automated quality assessment

– Determinations of which system components are most appropriate for given applications

– Intuitive interfaces

– Remote, unattended enrollment and recognition

� Non-interoperable Biometrics systems– Fully-interchangeable components

– Authenticity and data use restrictions

� ID theft related to biometrics templates– Can not be revoked and replaced to uniquely represent the source individual should that

individual’s template become compromised

– Transformed revocable and replaceable biometric templates created at time of capture

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IBM Research

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Enrollment issues

� What data should be collected at enrollment and how many samples?� How should they be stored: images/signals vs. features? � Batch vs. live enrollment issues� Enrollment for Positive Identification� Enrollment for Negative Identification (watch list)� De-enrollment policy� Input signal quality� The biometric zoo:

– Sheep (stable and well behaved)

– Goats (difficult to match=>large false rejects)

– Lambs (easy to be imitated=>large false accepts)

– Wolves (good at imitating others)

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IBM Research

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Public biometric databases

� Fingerprint– Rolled: NIST

– Flat: FVC 2000, FVC 2002, FVC 2004, EST� Face

– 2-D: FERET, FRGC, Olivetti, NIST 18,Yale, M2VTS

– 3-D: University of Notre Dame� Speaker

– NIST, TIMIT, NTIMIT� Iris

– CASIA (http://nlpr-web.ia.ac.cn/english/irds/irisdatabase.htm/)

– UPOL (http://phoenix.inf.upol.cz/iris/)� Signature

– SVC database (http://www.cs.ust.hk/svc2004/index.html#introduction)

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IBM Research

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Multi-biometrics

� More than one sample

� More than one sensing mode

� More than one biometric

� More than one matcher

� Multi-factor authentication?

� When is it useful, cost effective?

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Need for Multi-Biometric Integration� Large scale deployment of biometrics authentication and identification

cannot rely on a single biometrics because a single biometrics has a number of limitations, e.g.,

Accuracy: poor accuracy of individual biometrics can be improved by combining it with other weak biometrics.

Acceptability: some biometrics are not acceptable within some applications and target populations.

Availability: some biometrics are not available because of application domains or all individuals within population.

Security: More difficult to spoof or sabotage.� Smart Integration of multiple biometrics provides a way to overcome the

above limitations and make biometric deployments feasible.

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IBM Research

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Integration strategies

� Signal level using multiple samples and/or sensors

� Feature level: Integrate features to search

� Score level: Combine scores

� Decision level: Fuse decisions

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IBM Research

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Different Modes of Integration

© Bolle et al., Springer 2004

Signal levelFeature levelScore levelDecision level