Misalignment-robust face recognition

Authors:
Shuicheng Yan;Huan Wang;Jianzhuang Liu;Xiaoou Tang;Thomas S. Huang
Affiliations:
Department of Electrical and Computer Engineering, National University of Singapore, Singapore;Computer Science Department, Yale University, New Haven, CT;Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong and Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China;Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong and Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China;Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL
Venue:
IEEE Transactions on Image Processing
Year:
2010

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Abstract

Subspace learning techniques for face recognition have been widely studied in the past three decades. In this paper, we study the problem of general subspace-based face recognition under the scenarios with spatial misalignments and/or image occlusions. For a given subspace derived from training data in a supervised, unsupervised, or semi-supervised manner, the embedding of a new datum and its underlying spatial misalignment parameters are simultaneously inferred by solving a constrained l1 norm optimization problem, which minimizes the l1 error between the misalignment-amended image and the image reconstructed from the given subspace along with its principal complementary subspace. A byproduct of this formulation is the capability to detect the underlying image occlusions. Extensive experiments on spatial misalignment estimation, image occlusion detection, and face recognition with spatial misalignments and/or image occlusions all validate the effectiveness of our proposed general formulation for misalignment-robust face recognition.