Face recognition using fisher non-negative matrix factorization with sparseness constraints

Authors:
Xiaorong Pu;Zhang Yi;Ziming Zheng;Wei Zhou;Mao Ye
Affiliations:
Computational Intelligence Laboratory, School of Computer Science & Engineering, University of Electronic Science & Technology of China, Chegndu, Sichuan, China;Computational Intelligence Laboratory, School of Computer Science & Engineering, University of Electronic Science & Technology of China, Chegndu, Sichuan, China;Computational Intelligence Laboratory, School of Computer Science & Engineering, University of Electronic Science & Technology of China, Chegndu, Sichuan, China;Computational Intelligence Laboratory, School of Computer Science & Engineering, University of Electronic Science & Technology of China, Chegndu, Sichuan, China;Computational Intelligence Laboratory, School of Computer Science & Engineering, University of Electronic Science & Technology of China, Chegndu, Sichuan, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

A novel subspace method is proposed for part-based face recognition by using non-negative matrix factorization with sparseness constraints (NMFs) and Fisher's linear discriminant (FLD) hence its abbreviation, FNMFs. A comparative analysis engages PCA+FLD (FPCA) method and FNMFs method for both part-based and holistic-based face recognition. The comparative experiments are completed for the ORL face database and UMIST face database, it shows that FNMFs has better performance than FPCA-based method both for holistic-face and parts-face images recognition.