Hand Geometry

February 11, 2004 1

Hand Geometry

BIOM 426Instructor: 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 all of 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 hand geometry 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 inches between camera and platen.

Dimensions: - 8-1/2 inches square by 10 inches in height.

Scanner takes: - 96 measurements

Microprocessor converts: - 9-byte templateHand scanner optics.

February 11, 2004 8

EnrollmentDuring enrollment: - pins (pegs) help user to position his/her hand - user places his/her hand 3-5 times - scanner averages measurements and 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


Feature Extraction

An example feature set for hand geometry [4].


Metrics• Euclidean distance:

• Absolute distance:

Example:

Ei

d

iiE rqd

2

1

)(

Ai

d

iiA rqd

||1

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)

75.8222User15)(User2, Ed

1421.14)User2User2,( Ed 42)User2User2,( Ad

203)User15User2,( Ad


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/)


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.


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.


Positives and Negatives (See [5] pp. 146-147).


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)


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)


Preprocessing


Feature Extraction


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

Hand Geometry

Documents

Transcript of Hand Geometry