An Uncorrelated Space Algorithm Based on Fisher Minimum Criterion and Its Application to Face Recognition
YANG Jun1,2,LIU Yan-Li3,FENG Chao-Sheng1,FENG Lin1
1. College of Computer Science,Sichuan Normal University,Chengdu 610101 2. Institute of Graphics Images,College of Computer,Sichuan University,Chengdu 610064 3. College of Mathematics and Software Science,Sichuan Normal University,Chengdu 610101
Abstract Uncorrelated space algorithm is a fast method for extracting uncorrelated discriminant vectors based on the generalized fisher criterion,but it requires the total-scatter matrix to be reversible. To solve this problem,an improved uncorrelated features extraction method based on the generalized Fisher minimum criterion and uncorrelated space algorithm is proposed. The main idea of the proposed method is to solve the discriminant vectors of generalized fisher minimum criterion in the non-null subspace of the between-class scatter matrix. The rationality of the idea is discussed. A strategy including two steps is proposed to get the non-null subspace efficiently from high dimensional data. Firstly,the original data are mapped to a low dimensional subspace by PCA algorithm. Then,the non-null subspace of the between-class scatter matrix can be solved efficiently in the subspace,and the rationality of the process is discussed. The experimental results on standard face database show that the proposed method is efficient with higher accuracies compared with Fisherface algorithm and the uncorrelated space algorithm.
2013, Vol. 26 
