Exploring manifold structure of face images via multiple graphs | Zendy

Masheal Alghamdi | Zendy

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Exploring manifold structure of face images via multiple graphs

Author(s) -

Masheal Alghamdi

Publication year - 2013

Publication title -

proceedings of spie, the international society for optical engineering/proceedings of spie

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.192

H-Index - 176

eISSN - 1996-756X

pISSN - 0277-786X

DOI - 10.1117/12.2051527

Subject(s) - non negative matrix factorization , computer science , face (sociological concept) , artificial intelligence , matrix decomposition , pattern recognition (psychology) , graph , context (archaeology) , facial recognition system , image (mathematics) , selection (genetic algorithm) , factorization , algorithm , theoretical computer science , paleontology , social science , eigenvalues and eigenvectors , physics , quantum mechanics , sociology , biology

Geometric structure in the data provides important information for face image recognition and classification tasks. Graph regularized non-negative matrix factorization (GrNMF) performs well in this task. However, it is sensitive to the parameters selection. Wang et al. proposed multiple graph regularized non-negative matrix factorization (MultiGrNMF) to solve the parameter selection problem by testing it on medical images. In this paper, we introduce the MultiGrNMF algorithm in the context of still face Image classification, and conduct a comparative study of NMF, GrNMF, and MultiGrNMF using two well-known face databases. Experimental results show that MultiGrNMF outperforms NMF and GrNMF for most cases

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