Gaborface-based 2DPCA and (2D)2PCA classification with ensemble and multichannel model for face recognition

Authors:
Lin Wang;Yongping Li;Chengbo Wang;Hongzhou Zhang
Affiliations:
Chinese Academy of Sciences, Jiading, Shanghai, China;Chinese Academy of Sciences, Jiading, Shanghai, China;Chinese Academy of Sciences, Jiading, Shanghai, China;Chinese Academy of Sciences, Jiading, Shanghai, China
Venue:
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
Year:
2007

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Abstract

This paper introduces Gaborface-based 2DPCA and (2D)2PCA classification method based on 2D Gaborface matrices rather than transformed 1D feature vectors. Two kinds of strategies to use the bank of Gaborfaces are proposed: ensemble Gaborface representation (EGFR) and multichannel Gaborface representation (MGFR). The feasibility of our method is proved with the experimental results on the ORL and Yale databases. In particular, the MGFR-based (2D)2PCA method achieves 100% recognition accuracy for ORL database, and 98.89% accuracy for Yale database with five training samples per class.