Abstract
This paper aims to integrate part-based feature extractor, namely Non-negative matrix factorization (NMF), Local NMF and Spatially Confined NMF in wavelet frequency domain. Wavelet transform, with its approximate decomposition is used to reduce the noise and produce a representation in the low frequency domain, and hence making the facial images insensitive to facial expression and small occlusion. 75% ratio of full-face images are used for training and testing since they contain sufficient information as reported in a previous study. Our experiments on Essex-94 Database demonstrate that feature extractors in wavelet frequency domain perform better than without any filters. The optimum result is obtained for SFNMF of r* = 60 with Symlet orthonormal wavelet filter of order 2 in the second decomposition level. The recognition rate is equivalent to 98%.
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Neo, H.F., Teo, C.C., Teoh, A.B.J. (2010). A Wavelet-Based Face Recognition System Using Partial Information. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17277-9_44
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DOI: https://doi.org/10.1007/978-3-642-17277-9_44
Publisher Name: Springer, Berlin, Heidelberg
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