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

スポンサーリンク

概要

• 論文の詳細を見る

• 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.

• （社）電子情報通信学会の論文
• 2010-04-01

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

• Liu Qian

  Nanjing Univ. Of Information Sci. And Technol.

関連論文

• Facial Image Recognition Based on a Statistical Uncorrelated Near Class Discriminant Approach
• Facial Image Recognition Based on a Statistical Uncorrelated Near Class Discriminant Approach
• Face Recognition Based on Nonlinear DCT Discriminant Feature Extraction Using Improved Kernel DCV
• Sparsity Preserving Embedding with Manifold Learning and Discriminant Analysis

スポンサーリンク