Open Access

Kernel Learning of Histogram of Local Gabor Phase Patterns for Face Recognition

EURASIP Journal on Advances in Signal Processing20082008:469109

https://doi.org/10.1155/2008/469109

Received: 27 August 2007

Accepted: 4 February 2008

Published: 20 February 2008

Abstract

This paper proposes a new face recognition method, named kernel learning of histogram of local Gabor phase pattern (K-HLGPP), which is based on Daugman's method for iris recognition and the local XOR pattern (LXP) operator. Unlike traditional Gabor usage exploiting the magnitude part in face recognition, we encode the Gabor phase information for face classification by the quadrant bit coding (QBC) method. Two schemes are proposed for face recognition. One is based on the nearest-neighbor classifier with chi-square as the similarity measurement, and the other makes kernel discriminant analysis for HLGPP (K-HLGPP) using histogram intersection and Gaussian-weighted chi-square kernels. The comparative experiments show that K-HLGPP achieves a higher recognition rate than other well-known face recognition systems on the large-scale standard FERET, FERET200, and CAS-PEAL-R1 databases.

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Authors’ Affiliations

(1)
School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics
(2)
Computer Science and Engineering, Beijing Institute of Technology
(3)
Computer College, Harbin Institute of Technology

Copyright

© Baochang Zhang et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.