| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 94 === A fundamental problem of Non-negative Matrix Factorization (NMF) is that it does not always extract basis components manifesting localized features which are essential in face recognition. The aim of our work is to strengthen localized features in basis images and to impose orthonormal characteristic of Principle Component Analysis (PCA) on NMF. Such improved technique is called Basis-emphasized Non-negative Matrix Factorization (BNMF). In order to reduce noise disturbance in the original image such as facial expression, illumination variation and partial occlusion, Wavelet Transform (WT) is applied before the BNMF decomposition. In this paper, a novel subspace projection technique, called Basis-emphasized Non-negative Matrix Factorization with Wavelet Transform (wBNMF), is proposed to represent human facial image in low frequency sub-band and yields better recognition accuracy. These results are compared with those produced by PCA and NMF.
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