By Topic

Palm-Print Classification by Global Features

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Zhang, B. ; Dept. of Comput., Hong Kong Polytech. Univ., Hong Kong, China ; Wei Li ; Pei Qing ; Zhang, D.

Three-dimensional (3-D) palm print has proved to be a significant biometrics for personal authentication. Three-dimensional palm prints are harder to counterfeit than 2-D palm prints and more robust to variations in illumination and serious scrabbling on the palm surface. Previous work on 3-D palm-print recognition has concentrated on local features such as texture and lines. In this paper, we propose three novel global features of 3-D palm prints which describe shape information and can be used for coarse matching and indexing to improve the efficiency of palm-print recognition, particularly in very large databases. The three proposed shape features are maximum depth of palm center, horizontal cross-sectional area of different levels, and radial line length from the centroid to the boundary of 3-D palm-print horizontal cross section of different levels. We treat these features as a column vector and use orthogonal linear discriminant analysis to reduce their dimensionality. We then adopt two schemes: 1) coarse-level matching and 2) ranking support vector machine to improve the efficiency of palm-print recognition. We conducted a series of 3-D palm-print recognition experiments using an established 3-D palm-print database, and the results demonstrate that the proposed method can greatly reduce penetration rates.

Published in:

Systems, Man, and Cybernetics: Systems, IEEE Transactions on  (Volume:43 ,  Issue: 2 )
Biometrics Compendium, IEEE