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

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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Correspondence to Baochang Zhang.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Zhang, B., Wang, Z. & Zhong, B. Kernel Learning of Histogram of Local Gabor Phase Patterns for Face Recognition. EURASIP J. Adv. Signal Process. 2008, 469109 (2008). https://doi.org/10.1155/2008/469109

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Keywords

  • Face Recognition
  • Recognition Rate
  • Recognition System
  • Recognition Method
  • Phase Information
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