Skip to content


  • Research Article
  • Open Access

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

EURASIP Journal on Advances in Signal Processing20082008:469109

  • Received: 27 August 2007
  • Accepted: 4 February 2008
  • Published:


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.


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

Publisher note

To access the full article, please see PDF.

Authors’ Affiliations

School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing, 100080, China
Computer Science and Engineering, Beijing Institute of Technology, Beijing, 100080, China
Computer College, Harbin Institute of Technology, Harbin, 150001, China


© 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.