Facial Image Recognition Based on a Statistical Uncorrelated Near Class Discriminant Approach

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概要

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• In this letter, a statistical uncorrelated near class discriminant (SUNCD) approach is proposed for face recognition. The optimal discriminant vector obtained by this approach can differentiate one class and its near classes, i.e., its nearest neighbor classes, by constructing the specific between-class and within-class scatter matrices and using the Fisher criterion. In this manner, SUNCD acquires all discriminant vectors class by class. Furthermore, SUNCD makes every discriminant vector satisfy locally statistical uncorrelated constraints by using the corresponding class and part of its most neighboring classes. Experiments on the public AR face database demonstrate that the proposed approach outperforms several representative discriminant methods.

著者

• LI Sheng

  Nanjing University of Posts and Telecommunications

• JING Xiao-Yuan

  Nanjing University of Posts and Telecommunications

• BIAN Lu-Sha

  Nanjing University of Posts and Telecommunications

• GAO Shi-Qiang

  Nanjing University of Posts and Telecommunications

• LIU Qian

  Nanjing University of Posts and Telecommunications

• YAO Yong-Fang

  Nanjing University of Posts and Telecommunications

関連論文

• Facial Image Recognition Based on a Statistical Uncorrelated Near Class Discriminant Approach
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