A novel spatially confined non-negative matrix factorization for face recognition


Neo, H. F. and Ngo, C. L. and Teoh, B. J. (2005) A novel spatially confined non-negative matrix factorization for face recognition. MVA2005 IAPR Conference on Machine Vision Applications, 13 (16). pp. 502-505.

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In this paper, a novel method for facial representation called Spatially Confined Non-Negative Matrix Factorization (SFNMF) is presented. SFNMF aims to extract more spatially confined, parts-based representation from the NMF based representation by merely removing non-prominent region, and focalize on the salient feature. SFNMF derived a significant set of basis which allows a non-subtractive representation of images and these bases manifest localized features. Experimental results are presented to compare SFNMF with NMF and Local NMF. Advantageous of SFNMF is demonstrated when SFNMF achieves highest verification rate among the other.

Item Type: Article
Additional Information: In proceeding of: Proceedings of the IAPR Conference on Machine Vision Applications (IAPR MVA 2005), May 16-18, 2005, Tsukuba Science City, Jap
Subjects: Q Science > Q Science (General)
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 23 Jan 2014 04:57
Last Modified: 23 Jan 2014 04:57
URII: http://shdl.mmu.edu.my/id/eprint/4970


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