论文名称：Robust spectral regression for face recognition
论文类型：期刊论文
发表刊物：NEUROCOMPUTING
收录刊物：SCIE、EI、Scopus
卷号：118
页面范围：33-40
ISSN号：0925-2312
关键字：Discriminant subspace learning; Face recognition; Spectral regression; Robust; Correntropy
摘要：Spectral regression has been an efficient and powerful tool for face recognition. However, spectral regression is sensitive to the errors incurred by inaccurate annotation and occlusion. This paper studies robust spectral regression based discriminant subspace learning from correntropy and spatially smooth structure of facial subspace. First, we formulate the robust discriminant subspace learning problem as a maximum correntropy problem, which finds the most correlation solution between spectral targets and predictions. Second, total variation (TV) regularization is imposed on the correntropy objective to learn a spatially smooth face structure. Lastly, based on the additive form of half-quadratic optimization, we cast the maximum correntropy problem into a compound regularization model, which can be efficiently optimized via an accelerated proximal gradient algorithm. Compared with iteratively reweighted least squares based methods, the proposed method can not only improve recognition rates but also reduce computational cost. Experimental results on a couple of face recognition datasets demonstrate the robustness and effectiveness of our method against inaccurate annotation and occlusion. (C) 2013 Elsevier B.V. All rights reserved.
发表时间：2013-10-22