Face Recognition Using (2D)^2PCA and Wavelet Packet Decomposition

The correct recognition rate (CRR) and implementation speed are two evaluation criteria for face recognition system. However, it is difficult to boost them when images are taken under different conditions. In this paper, the performance of a recognition method using wavelet packet decomposition (WPD) and two-directional two-dimensional principal component analysis ((2D)
²
PCA) is explored. First, plot images are obtained via two-level WPD on original image. And then, the feature matrixes of these plot images are extracted using (2D)2PCA. Finally, the method is constructed by fusing the feature matrixes of dasiasuccessfulpsila plot images properly chosen. Experiments on images with different illumination, expressions, and poses from PIE, Yale, and UMIST indicate that the proposed method can get a higher correct recognition rate than performing (2D)
²
PCA on original image.