Fuzzy Regularized Linear Discriminant Analysis for Face Recognition

被引：0
|
作者：
Taghlidabad, Mehran Aghaei [1 ]
Salehi, Negar Baseri [1 ]
Kasaei, Shohreh [2 ]
机构：
[1] Int Sharif Univ Technol, Dept Elect Engn, Kish Isl, Iran
[2] Sharif Univ Technol, Dept Comp Engn, Tehran, Iran
来源：
FOURTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2011): MACHINE VISION, IMAGE PROCESSING, AND PATTERN ANALYSIS | 2012年 / 8349卷
关键词：
Face recognition; fuzzy regularized linear Discriminant analysis (FR-LDA); regularized linear Discriminant analysis (R-LDA); fuzzy set theory; ALGORITHM;
D O I：
10.1117/12.920913
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A new face recognition method is proposed in this paper. The proposed method is based on fuzzy regularized linear discriminant analysis (FR-LDA) and combines the regularized linear discriminant analysis (R-LDA) and the fuzzy set theory. R-LDA is based on a new regularized Fisher's discriminant criterion, which is particularly robust against the small sample size problem compared to the traditional one used in LDA. In the proposed method, we calculate the membership degree matrix by Fuzzy K-nearest neighbor (FKNN) and then incorporate the membership degree into the definition of the between-class and within-class scatter matrices and get the fuzzy between-class and within-class scatter matrices. Experimental results obtained on the FERET database demonstrate that the proposed method improves the classification rate performance.
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收藏
页数：6
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