Discriminant waveletfaces and nearest feature classifiers for face recognition

Feature extraction, discriminant analysis, and classification rule are three crucial issues for face recognition. This paper presents hybrid approaches to handle three issues together. For feature extraction, we apply the multiresolution wavelet transform to extract waveletface. We also perform the linear discriminant analysis on waveletfaces to reinforce discriminant power. During classification, the nearest feature plane (NFP) and nearest feature space (NFS) classifiers are explored for robust decision in presence of wide facial variations. Their relationships to conventional nearest neighbor and nearest feature line classifiers are demonstrated. In the experiments, the discriminant waveletface incorporated with the NFS classifier achieves the best face recognition performance.