Robust spectral regression for face recognition

  • Authors:

  • Yanqing Guo;Ran He;Wei-Shi Zheng;Xiangwei Kong;Zhaofeng He

  • Affiliations:

  • School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China and Postdoctoral Scientific Research Workstation, State Information Center, Beijing 10004 ...;National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, Beijing 100190, China;School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510275, China;School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China;Irisking Co., Ltd., Beijing 100190, China

  • Venue:
  • Neurocomputing
  • Year:
  • 2013

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Abstract

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.