Face feature extraction and recognition based on discriminant subclass-center manifold preserving projection

  • Authors:

  • Xiao-Yuan Jing;Chao Lan;David Zhang;Jing-Yu Yang;Min Li;Sheng Li;Song-Hao Zhu

  • Affiliations:

  • State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China and College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China and State Key Laboratory ...;College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China;Department of Computing, Hong Kong Polytechnic University, Hong Kong;College of Computer Science, Nanjing University of Science and Technology, Nanjing, China;College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China;College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China;College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2012

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Abstract

Manifold learning is an effective dimensional reduction technique for face feature extraction, which, generally speaking, tends to preserve the local neighborhood structures of given samples. However, neighbors of a sample often comprise more inter-class data than intra-class data, which is an undesirable effect for classification. In this paper, we address this problem by proposing a subclass-center based manifold preserving projection (SMPP) approach, which aims at preserving the local neighborhood structure of subclass-centers instead of given samples. We theoretically show from a probability perspective that, neighbors of a subclass-center would comprise of more intra-class data than inter-class data, and thus is more desirable for classification. In order to take full advantage of the class separability, we further propose the discriminant SMPP (DSMPP) approach, which incorporates the subclass discriminant analysis (SDA) technique to SMPP. In contrast to related discriminant manifold learning methods, DSMPP is formulated as a dual-objective optimization problem and we present analytical solution to it. Experimental results on the public AR, FERET and CAS-PEAL face databases demonstrate that the proposed approaches are more effective than related manifold learning and discriminant manifold learning methods in classification performance.