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%. © 2010 Springer-Verlag.
CITATION STYLE
Neo, H. F., Teo, C. C., & Teoh, A. B. J. (2010). A wavelet-based face recognition system using partial information. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6455 LNCS, pp. 427–436). https://doi.org/10.1007/978-3-642-17277-9_44
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