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AbstractGeometric 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.
CitationAlghamdi, Masheal. "Exploring manifold structure of face images via multiple graphs." In Sixth International Conference on Machine Vision (ICMV 13), pp. 90671D-90671D. International Society for Optics and Photonics, 2013
PublisherSPIE-Intl Soc Optical Eng
Conference/Event name6th International Conference on Machine Vision, ICMV 2013