Face Recognition Using Improved Null Space Method Based on DCT
基于DCT的改进零空间人脸识别算法

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.