改进的线性判别分析算法及其在人脸识别中的应用

A Modified Lanear Discriminant Analysis and Its Application to Face Recognition

提出了一种改进的线性判别分析(LDA)算法,能有效地解决传统LDA算法的两个局限,即小样本问题及在多类情况下传统的Fisher准则非最优.该算法还能提高某一(几)个指定类别的分类率.这种算法的关键在于使用不损失"有判别力信息"的方法来降维,同时在传统的Fisher准则中引入加权函数,得到与分类率直接相关的改进准则.在ORL人脸数据库上的比较实验结果证实了该算法的有效性.

This paper presented an improved linear discriminant analysis (LDA) algorithm for face recognition, which can effectively deal with the two problems in traditional LDA-based approaches: ①the small sample size problem, and ② the Fisher criterion is nonoptimal with respect to classification rate. In particular, the proposed algorithm can also improve the classification rate of one or several appointed classes. The key to this method is to use the technique that it can reserves the 'significant discriminatory information' for dimension reduction and meanwhile utilize a modified Fisher criterion. The comparative experiments on ORL face database verify the effectiveness of the proposed method.