Abstract
This paper proposes a method for face recognition by integrating non-negative matrix factorization with sparseness constraints (NMFs) and radial basis function (RBF) classifier. NMFs can represent a facial image based on either local or holistic features by constraining the sparseness of the basis images. The comparative experiments are carried out between NMFs with low or high sparseness and principle component analysis (PCA) for recognizing faces with or without occlusions. The simulation results show that RBF classifier outperforms k–nearest neighbor linear classifier significantly in recognizing faces with occlusions, and the holistic representations are generally less sensitive to occlusions or noise than parts-based representations.
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Zhou, W., Pu, X., Zheng, Z. (2006). Parts-Based Holistic Face Recognition with RBF Neural Networks. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_17
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DOI: https://doi.org/10.1007/11760023_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
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