An Approach to Image Dimension Reduction and Its Application to Face Images
Xu Yong①; Yang Jian②; Zhao Ying-nan③; Song Feng-xi①; Yang Jing-yu④
①Department of Computer Science & Technology, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China; ②The Biometrics Research Center and Department of Computing, Hong Kong Polytechnic University, Hong Kong, China; ③Department of Physics Science & Information Engineering, Jishou University, Jishou 416000, China; ④Department of Computer Science & Technology, Nanjing University of Science & Technology, Nanjing 210094, China
Abstract：As a technique of feature extraction, 2DPCA is effective and efficient. Different from traditional PCA, it directly computes projection of one image matrix onto vector, to obtain feature for the image. In fact, 2DPCA is optimal for dimension compression under this consideration. There are two approaches to implement 2DPCA. The two approaches transform images into different spaces, and emphasize horizontal feature and vertical feature of face images respectively. Because the features extracted by the two approaches may complement each other, two schemes are designed to perform feature fusion. Experiments based on the fused features achieve high classification right rates.
徐 勇; 杨 健;赵英男; 宋枫溪; 杨静宇 . 一种缩减图像维数的方法及其在人脸图像上的应用[J]. 电子与信息学报, 2008, 30(1): 180-184 .
Xu Yong①; Yang Jian②; Zhao Ying-nan③; Song Feng-xi①; Yang Jing-yu④. An Approach to Image Dimension Reduction and Its Application to Face Images. , 2008, 30(1): 180-184 .
2008, Vol. 30 
