Exploring manifold structure of face images via multiple graphs

Masheal Alghamdi

doi:10.1117/12.2051527

24 December 2013 Exploring manifold structure of face images via multiple graphs

Masheal Alghamdi

Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90671D (2013) https://doi.org/10.1117/12.2051527
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom

Abstract

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.

Citation Download Citation

Masheal Alghamdi "Exploring manifold structure of face images via multiple graphs", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90671D (24 December 2013); https://doi.org/10.1117/12.2051527

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