L. Wang, Y. Li, C. Wang, and H. Zhang (PRC)
Face Recognition; Gaborface, 2D Principal Component Analysis (PCA), (2D)2 PCA, Multichannel, Decision Level Fusion
This paper introduces Gaborface-based 2DPCA and (2D)2 PCA 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)2 PCA method achieves 100% recognition accuracy for ORL database, and 98.89% accuracy for Yale database with five training samples per class.
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