Abstract
This paper proposes an algorithm on a face classification by using 2D wavelet subband transform and nonsingular fisher discriminant analysis for a face recognition. For a feature extraction, we apply the multiresolution wavelet transform to extract waveletfaces. We also perform the linear discriminant on waveletfaces to reinforce the discriminant power. During classification, the nonsingular fisher discriminant waveletfaces are used. In this study, we found that NDW (Nonsingular Discriminant Waveletface) solves the small sample size matter. Thus, NDW is superior to LDA for an efficient face classification.
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Kim, J.O., Chung, K.H., Chung, C.H. (2005). Optimal Face Classification by Using Nonsingular Discriminant Waveletfaces for a Face Recognition. In: Fox, E.A., Neuhold, E.J., Premsmit, P., Wuwongse, V. (eds) Digital Libraries: Implementing Strategies and Sharing Experiences. ICADL 2005. Lecture Notes in Computer Science, vol 3815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11599517_65
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DOI: https://doi.org/10.1007/11599517_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30850-8
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