Based on an Optimized LDA Algorithm for Face Recognition
基于优化的LDA算法人脸识别研究

Extracting the most discriminant low-dimensional face feature is an extremely critical step in Face Recognition(FR)systems.Linear Discriminant Analysis(LDA)is one of the most popular linear classification techniques for feature extraction.An optimized LDA algorithm is introduced to overcome questions existing in the traditional LDA algorithm for FR in this paper.The between-class scatter matrix is redefined in order to make the traditional Fisher criterion optimal and eliminate the effect that the edge of class has on selecting the optimal projection;at the same time,it avoids computing the inverse of matrix by means of factorization,and solves the Small Sample Size(SSS)problem.Adopting experiential method,the appropriate value of e is found,and then the optimal effect of face recognition is got.Experimental results show the recognition rate of this method is superior to the traditional LDA.