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Hand Geometry

Hand Geometry. BIOM 426 Instructor: Natalia A. Schmid. Outline. Motivation Acquisition systems Enrollment Verification Feature Extraction Metrics Applications Privacy. References. Not much open literature is available.

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Hand Geometry

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  1. Hand Geometry BIOM 426 Instructor: Natalia A. Schmid February 11, 2004

  2. Outline • Motivation • Acquisition systems • Enrollment • Verification • Feature Extraction • Metrics • Applications • Privacy February 11, 2004

  3. References • Not much open literature is available. • Much information is in the form of: • Patents (for example: Miller’71, Sidlauskas’88) • Application-oriented descriptions (see IEEE Spectrum no 2, 1994) • Exclusion: prototype system described by Jain et al. [4] • Web pages of Recognition Systems and Biomet.ch • Tutorials (for example, BFC) February 11, 2004

  4. Motivation • Attractive points: • Almost allof the working population has hand; • Exception processing can be easily engineered; • Measurements are easily collectable; • Non-intrusive compared to iris or retinal scan; Simple method of sensing Computations are easy => system is easy to build Easy to integrate with other biometrics as fingerprint Storage efficient (9 bytes) February 11, 2004

  5. Evolution • First devices (1960s) were electromechanical.(Miller’s “Identimation”) - measures length of 4 fingers - used in nuclear weapon industry - was retired in 1987 • In the mid-1980’s Sidlauskas developed electronic 3D profile identification apparatus. - capacity 20,000 users - processing time is 1.2 sec. (1994) - weight is 4.5 kg (1994) - 9-byte representation The existing handgeometry systems rely on visual images of the hand. February 11, 2004

  6. Acquisition systems Features: - finger length, width, thickness, curvatures and relative location of features. Scanners use: - CCD camera, infrared LEDs, mirrors and reflectors. - No surface details, no color, no fingerprint lines is recorded. - Top and side views. 32,000 pixel field February 11, 2004

  7. Acquisition systems Scanners use: - Optical path approx. 11 inchesbetween camera and platen. Dimensions: - 8-1/2 inches square by 10 inches in height. Scanner takes: - 96 measurements Microprocessor converts: - 9-byte template Hand scanner optics. February 11, 2004

  8. Enrollment During enrollment: - pins (pegs) help user to position his/her hand - user places his/her hand 3-5 times - scanner averages measurementsand stores in the database Quality of enrollment affects FRR Factors: - platen heights - training (for example, “landing an airplane scenario”) Template averaging: - updating template after user is verified February 11, 2004

  9. Verification • User types PIN (key pad) • Places hand on the platen Scanner - takes measurements - extracts features - compares previous template with the input template - generates a similarity score February 11, 2004

  10. Feature Extraction Typical image: black-and-white Features: finger length, width, thickness, curvatures and relative location of features February 11, 2004

  11. Feature Extraction An example feature set for hand geometry [4]. February 11, 2004

  12. Metrics • Euclidean distance: • Absolute distance: Example: User 2 = (71, 63, 70, 61, 74, 56, 56, 52, 281, 362, 268, 278, 243, 136)User 2 = (69, 63, 74, 62, 73, 57, 57, 55, 276, 366, 259, 282, 245, 141)User 15 = (55, 56, 63, 53, 60, 47, 48, 47, 249, 303, 258, 268, 241, 152) February 11, 2004

  13. Market Access Control Used to access Health clubs, Day care centers, Laboratories, Prisons, etc. Time & Attendance Application ranges from coal mines to clean rooms. Personal Identification Newark and Toronto airports; Food Services systems at the University of Georgia (See more on http://www.recogsys.com/) February 13, 2004

  14. Applications • 70,000 HandReaders are installed throughout the world. • The 1996 Olympic Games used HandReaders to protect access to Olympic Village (65,000 people were enrolled; 1 million transactions were handled over 28 days). • Since 1991, at San Francisco Airport, HandReaders produced more than 100 million verifications (180 doors and 18,000 employees). • In the United Kingdom, Her majesty’s Prisons rely on the HandReaders for prisoner and visitor tracing. • Colleges (ex. University of Georgia) use HandReaders for on-campus meal programs, safeguard access to dormitories and protect their computer centers. • Over 20,000 Owens Illinois employees punch in and out each day using the HandReader. • Krispy Cream Doughnuts uses HandReaders for tracking employee hours at over 30 individual stores. February 13, 2004

  15. 1. Privacy Issues Hand geometry is used to verify identity. Templates cannot be “reverse engineered” to identify users. 2.Operation by Disabled People Hand scanners can be used for scanning left hand (palm up). Could be enabled for blind persons to use. February 13, 2004

  16. (See [5] pp. 146-147). Positives and Negatives February 13, 2004

  17. Available Databases 1. University of Bologna database http://bias.csr.unibo.it/research/biolab/bio_tree.html 2. MSU hand geometry database. 3. Ongoing project at WVU (multi-modal biometrics) February 13, 2004

  18. References 1. Biometrics: Personal Identification in Networked Society, A. Jain et al. Edt. 2. Hand: give me five by D. Sidlauskas in “Vital signs of identity,” IEEE Specrtum, February 1994, pp.24 - 25. 3. D. P. Sidlauskas, “3D hand profile identification apparatus,” US Patent No. 4736203, 1988. 4. A. K. Jain, A. Ross, and Sh. Pankanti, “A Prototype Hand Geometry-based Verification System,” Proc. of 2nd Int’l Conf. on Audio- and Video-based Biometric Person Authentication, Washington D.C., pp. 166-171, March 22-24, 1999. 5. R. M. Bolle, et al., Guide to Biometrics, Springer, New York, 2004, pp. 45-47. 6. http://www.recogsys.com/ 7. http://www.biomet.ch/ (two-finger verification) February 13, 2004

  19. Preprocessing February 13, 2004

  20. Feature Extraction February 13, 2004

  21. Feature Extraction Matlab Code: >> IM = imread(‘filename’,‘tiff’); % read tiff-file >> BW = im2bw(IM,0.75); % binarization >> size(IM) % provides info. about image size >> mask = zeros(512,640); % creates image filled with zeros >> mask (260,190:450) = 1; % fills line with ones >> Feature = (1-BW).*mask; % extracts feature >> length(find(Feature > 0)) % finds feature length in pixels February 13, 2004

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