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<p><b>Abstract</b>—<it>Feature extraction</it>, <it>discriminant analysis,</it> and <it>classification rule</it> 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 <it>waveletface</it>. We also perform the <it>linear discriminant analysis</it> on waveletfaces to reinforce discriminant power. During classification, the <it>nearest feature plane</it> (NFP) and <it>nearest feature space</it> (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.</p>
Discriminant waveletface, nearest feature classifier, face recognition.

J. Chien and C. Wu, "Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 24, no. , pp. 1644-1649, 2002.
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