中文摘要：

线性判别分析（LDA）是一种较为普遍的线性特征提取方法，它的主要缺点是在应用时经常遇到小样本问题，同时其准则函数并不与识别率直接相关。该文提出一种基于DCT的改进零空间LDA方法，能够解决以上两个问题。首先，通过使用DCT代替“像素聚类”并重新定义类间散布矩阵，得到一种新的零空间法。然后将这种方法与F-LDA结合起来得到一种新的对人脸识别更有效的特征提取方法，实验证明这种方法能得到较好的识别率。

英文摘要：

Linear Discriminant Analysis（LDA） is one of the most popular linear projection techniques for feature extraction. The major drawback of applying LDA is that it often encounters the Small Sample Size（SSS） problem. Besides, their optimization criteria is not directly related to the classification accuracy. In this paper, an improved null space LDA method based on DCT is proposed to solve both problems. First, by employing the DCT instead of the ＂pixel grouping＂ and redefining the within class scatter matrix, a new null space method is given. Then, combining this method with F-LDA an efficient new feature extraction algrithm is proposed for face recognition. Experimental results show that this method achieves better performance than existing ones.