Exploring manifold structure of face images via multiple graphs

Masheal M. Alghamdi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish (US)
Title of host publicationSixth International Conference on Machine Vision (ICMV 2013)
PublisherSPIE-Intl Soc Optical Eng
ISBN (Print)9780819499967
StatePublished - Dec 24 2013

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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