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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Facial Feature Extraction Based on Robust PCA and Histogram
verfasst von : Xiao Luan, Weisheng Li
Erschienen in: Pattern Recognition
Verlag: Springer Berlin Heidelberg
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Inspired by recently-proposed robust principal component analysis (RPCA), in this paper we present a feature extraction method for robust face recognition in the presence of random pixel corruption and occlusion. Unlike most work focusing on the low-rank structure recovered by RPCA, we consider that the sparse error component contains more discriminating power which is essential to face recognition. In order to illustrate the intensity distribution of the sparse error component, a histogram-based sparsity measure is introduced for feature extraction. Compared with the related state-of-the-art methods, experimental results on Extended Yale B database verify the advancement of the proposed method for partially corrupted and occluded face images.