A survey of image-based biometric identification methods:Face, finger print, iris, and others

Presented by: David Lin

ECE738 Presentation of Project Survey

© 2003 by David Lin 2

Outline

• Problems and motivations

• Different identification methods– Face Recognition– Fingerprints– Iris Recognition– Hand Geometry– Others

• Summary and Conclusions

© 2003 by David Lin 3

Problems

• Security has always been an important concern to many people. Such as banks, industrial, military systems, and personal information.

• Traditional security and identification are base on things that can be easily breached. Knowledge based or token based.

• Not unique, can be duplicated, e.g. Passwords and ID cards.

© 2003 by David Lin 4

Biometrics System

• Identity verification of living, human individuals based on physiological and behavioral characteristics.

• “Something you are or you do”

• In general, biometric system is not easily duplicated and unique to each individuals

© 2003 by David Lin 5

Biometrics System

• What should we look for in Biometrics systems?– Universality, which means that each person should ha

ve the characteristic– Uniqueness, which indicates that no two persons sho

uld be the same in terms of the characteristic– Permanence, which means that the characteristic sho

uld not be changeable– Collectability, which indicates that the characteristic c

an be measured quantitatively

© 2003 by David Lin 6

Face Recognition

• Techniques such as, Eigenfaces, geometry representation, Gabor wavelet transform, Karhunen-Loeve, etc.

• Acquisitions - frontal view, half profile, profile view.

• Affected by facial beard, glasses, hair style, age.

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Fingerprints

• Most of the existing systems uses “minutiae” in a fingerprint image for matching.

• Minutiae are the details in the fingerprint ridges, ridge endings and bifurcations.

Endings

Bifurcations

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Fingerprints

111

101

111

Extraction Filter

1 = ending2 = ridge3 = bifurcation

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Iris Recognition

• The highly randomized appearance of the iris makes its use as a biometric well recognized. Its suitability as an exceptionally accurate biometric derives from its,– extremely data-rich physical structure,– genetic independence, no two eyes are the same– stability over time– physical protection by a transparent window (the corn

ea) that does not inhibit external viewability.

© 2003 by David Lin 10

Iris Recognition

• Daugman Method, zero-crossing 1D wavelet transform, multi-channel Gabor filtering

• Most of them uses Gabor wavelets filter• Iris code is calculated using circular bands that

have been adjusted to conform to the iris and pupil boundaries.

• Eyelashes or the eyelid obscure part of the grid might influence system operations

© 2003 by David Lin 11

Multi-channel Gabor filtering

Extracted block is 512 x 64 pixelsDaugman MethodEight circular band512-byte iris code

© 2003 by David Lin 12

Hand Geometry

Different views of the prototype designed: (a) Platform and camera, (b) placement of the user's hand, and (c) photograph taken.

Measurements• Widths• Heights• Deviations• Angles

Classifiers• Euclidean Distance• Hamming Distance• Gaussian Mixture Models

GMM shows thebest result

© 2003 by David Lin 13

Hand Vein Patterns

• Hand vein pattern is distinctive for various individuals.

• The veins under the skin absorb infrared light and thus have a darker pattern on the image of the hand taken by an infrared camera.

• One system is manufactured by British Technology Group is called Veincheck and uses a template with the size of 50 bytes.

Back of the hand

© 2003 by David Lin 14

Retinal Patterns

• Uses the vascular patterns of the retina of the eye.

• In healthy individuals, the vascular pattern in the retina does not change over the course of an individual's life.

• The patterns are scanned using a low-intensity (e.g. near-infrared) light source.

© 2003 by David Lin 15

Retinal Patterns

• The main drawback of the retina scan is its intrusiveness. A laser light must be directed through the cornea of the eye.

• Operation of the retina scanner is not easy.

• The size of the eye signature template is 96 bytes.

© 2003 by David Lin 16

Signature

• Uses the dynamic analysis of a signature to authenticate a person.

• Measuring dynamic features such as speed, pressure and angle used when a person signs a standard, recorded pattern (e.g. autograph).

Captured using a tablet

• One focus for this technology has been e-business applications and other applications where a signature is an already accepted method of personal authentication.

© 2003 by David Lin 17

Summary & ConclusionsEase of

useError

incidenceAccuracy User

acceptanceRequired security

level

Long-term

stability

Fingerprint High Dryness, dirt

High Medium High High

Hand Geometry

High Hand injury, age

High Medium Medium Medium

Iris Medium Poor Lighting

Very High Medium Very High High

Retina Low Glasses Very High Medium High High

Signature High Changing signatures

High Very high Medium Medium

Face Medium Lighting, age, hair, glasses

High Medium Medium Medium

© 2003 by David Lin 18

Summary & Conclusions

• By combining two or more individual biometric systems cheaper and reliable security can be obtained.