Membership-degree preserving discriminant analysis with applications to face recognition.
In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data. In this paper, we propose a novel feature extraction algorithm called membership-degree preserving discriminant analysis (MPDA) based on the fisher criterion and fuzzy set theory for face recognition. In the proposed algorithm, the membership degree of each sample to particular classes is firstly calculated by the fuzzy k-nearest neighbor (FKNN) algorithm to characterize the similarity between each sample and class centers, and then the membership degree is incorporated into the definition of the between-class scatter and the within-class scatter. The feature extraction criterion via maximizing the ratio of the between-class scatter to the within-class scatter is applied. Experimental results on the ORL, Yale, and FERET face databases demonstrate the effectiveness of the proposed algorithm.
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Zhangjing
Z
Yang
Chuancai
C
Liu
Pu
P
Huang
Jianjun
J
Qian
D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D016002:Discriminant Analysis; D005145:Face; D017143:Fuzzy Logic; D006801:Humans; D016014:Linear Models; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data*