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Topology preserving non-negative matrix factorization for face recognition
Zhang T.; Fang B.; Tang Y.Y.; He G.; Wen J.
2008-04-01
Source PublicationIEEE Transactions on Image Processing
ISSN10577149
Volume17Issue:4Pages:574-584
AbstractIn this paper, a novel topology preserving non-negative matrix factorization (TPNMF) method is proposed for face recognition. We derive the TPNMF model from original NMF algorithm by preserving local topology structure. The TPNMF is based on minimizing the constraint gradient distance in the high-dimensional space. Compared with L distance, the gradient distance is able to reveal latent manifold structure of face patterns. By using TPNMF decomposition, the high-dimensional face space is transformed into a local topology preserving subspace for face recognition. In comparison with PCA, LDA, and original NMF, which search only the Euclidean structure of face space, the proposed TPNMF finds an embedding that preserves local topology information, such as edges and texture. Theoretical analysis and derivation given also validate the property of TPNMF. Experimental results on three different databases, containing more than 12 000 face images under varying in lighting, facial expression, and pose, show that the proposed TPNMF approach provides a better representation of face patterns and achieves higher recognition rates than NMF. © 2008 IEEE.
KeywordFace recognition Manifold structure Non-negative matrix factorization Topology preserving
DOI10.1109/TIP.2008.918957
URLView the original
Language英語
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被引频次[WOS]:99   [WOS记录]     [WOS相关记录]
Document TypeJournal article
专题University of Macau
AffiliationChongqing University
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Zhang T.,Fang B.,Tang Y.Y.,et al. Topology preserving non-negative matrix factorization for face recognition[J]. IEEE Transactions on Image Processing,2008,17(4):574-584.
APA Zhang T.,Fang B.,Tang Y.Y.,He G.,&Wen J..(2008).Topology preserving non-negative matrix factorization for face recognition.IEEE Transactions on Image Processing,17(4),574-584.
MLA Zhang T.,et al."Topology preserving non-negative matrix factorization for face recognition".IEEE Transactions on Image Processing 17.4(2008):574-584.
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