Robust discriminative nonnegative patch alignment for occluded face recognition
Ou, Weihua1; Li, Gai2; Yu, Shujian3; Xie, Gang1; Ren, Fujia1; Tang, Yuanyan4
Conference Name22nd International Conference on Neural Information Processing, ICONIP 2015
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference Date11 9, 2015 - 11 12, 2015
Conference PlaceIstanbul, Turkey
Author of SourceSpringer Verlag
AbstractFace occlusion is one of the most challenging problems for robust face recognition. Nonnegative matrix factorization (NMF) has been widely used in local feature extraction for computer vision. However, standard NMF is not robust to occlusion. In this paper, we propose a robust discriminative representation learning method under nonnegative patch alignment, which can take account of the geometric structure and discriminative information simultaneously. Specifically, we utilize linear reconstruction coefficients to characterize local geometric structure and maximize the pair wise fisher distance to improve the separability of different classes. The reconstruction errors are measured with weighted distance, and the weights for each pixel are learned adaptively with our proposed update rule. Experimental results on two benchmark datasets demonstrate the learned representation is more discriminative and robust than most of the existing methods in occluded face recognition. © Springer International Publishing Switzerland 2015.
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Cited Times [WOS]:4   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.School of Mathematics and Computer Science, Guizhou Normal University, Guiyang, China;
2.Department of Electronics and Information Engineering, Shunde Polytechnic, Foshan, China;
3.Department of Electrical and Computer Engineering, University of Florida, Gainesville, United States;
4.University of Macau, Macau, China
Recommended Citation
GB/T 7714
Ou, Weihua,Li, Gai,Yu, Shujian,et al. Robust discriminative nonnegative patch alignment for occluded face recognition[C]//Springer Verlag,2015:207-215.
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