基于二维线性判别分析的人脸识别算法研究

In the existing mature face recognition algorithms, the principal component analysis (PCA) method focuses on compression, is not conducive to the classification. The linear discriminant analysis (LDA) algorithm, although suitable for classification, but there are problems of small samples. PCA + LDA algorithm use PCA to reduce the dimension of the original image firstly, and to make the class scatter matrix nonsingular, then use LDA method for feature extraction. However, this method will also lose some image information when using PCA to reduce dimension, and is not good for the subsequent classification. To solve this problem. This paper studied the two-dimensional linear discriminant analysis method(2DLDA).The method obtaineed the within and between-class scatter matrix from the face image matrices directly, and does not need to change face images into vector matrix, not only solved the small sample size problem, but also reduced the amount of computation. Experimental results show that 2DLDA algorithm has certain advantages compared to the PCA, PCA + LDA algorithm.