1. A survey of image-based biometric identification methods: Face, finger print, iris, and others Presented by: David Lin ECE738 Presentation of Project Survey

2. © 2003 by David Lin2 Outline Problems and motivations Different identification methods –Face Recognition –Fingerprints –Iris Recognition –Hand Geometry –Others Summary and Conclusions

3. © 2003 by David Lin3 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.

4. © 2003 by David Lin4 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

5. © 2003 by David Lin5 Biometrics System What should we look for in Biometrics systems? –Universality, which means that each person should have the characteristic –Uniqueness, which indicates that no two persons should be the same in terms of the characteristic –Permanence, which means that the characteristic should not be changeable –Collectability, which indicates that the characteristic can be measured quantitatively

6. © 2003 by David Lin6 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.

7. © 2003 by David Lin7 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

8. © 2003 by David Lin8 Fingerprints Extraction Filter 1 = ending 2 = ridge 3 = bifurcation

9. © 2003 by David Lin9 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 cornea) that does not inhibit external viewability.

10. © 2003 by David Lin10 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

11. © 2003 by David Lin11 Multi-channel Gabor filtering Extracted block is 512 x 64 pixels Daugman Method Eight circular band 512-byte iris code

12. © 2003 by David Lin12 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 the best result

13. © 2003 by David Lin13 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

14. © 2003 by David Lin14 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.

15. © 2003 by David Lin15 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.

16. © 2003 by David Lin16 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.

17. © 2003 by David Lin17 Summary & Conclusions Ease of use Error incidence AccuracyUser acceptance Required security level Long- term stability FingerprintHighDryness, dirtHighMediumHigh Hand Geometry HighHand injury, age HighMedium IrisMediumPoor Lighting Very HighMediumVery HighHigh RetinaLowGlassesVery HighMediumHigh SignatureHighChanging signatures HighVery highMedium FaceMediumLighting, age, hair, glasses HighMedium

18. © 2003 by David Lin18 Summary & Conclusions By combining two or more individual biometric systems cheaper and reliable security can be obtained.