2D Gaborface representation method for face recognition with ensemble and multichannel model

This paper proposes a scheme that is based on linear correlation criterion to select optimized Gabor filter bank. In addition, by using 2D Gaborface matrices rather than transformed 1D feature vectors, a novel Gaborface-based 2DPCA and (2D)2PCA classification method is introduced. 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, Yale and FERET 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, and 99.5% accuracy for FERET database.