LOCAL CORRELATION CLASSIFICATION AND ITS APPLICATION TO FACE RECOGNITION ACROSS ILLUMINATION
In this paper, a real face image is regarded as the result of adding the so-called standard face image under an ideal illumination condition to the corresponding error image, which reflects the imaging difference between the real illumination and the ideal illumination. Furthermore, based on two propositions, we infer that for two images of the same face the correlation between two corresponding areas of the two images will be great enough, while the one between two corresponding areas of two face images of two different individuals will be low. From the viewpoint, a classification algorithm, which is based on a specific definition of correlation between two image areas, is developed. It is computationally tractable and may be regarded as one normalization method.Differing from other normalization methods, this algorithm need not explicitly normalize one face image. The experiment shows that the algorithm is efficient and very excellent for categorizing frontal faces with varying illuminations.
Face recognition varying illuminations PCA local correlation classification
YONG XU JING-YU YANG ZHONG JIN YU-JIE ZHENG
Bio-computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 51 Department of computer science & technology, Nanjing University of Science & Technology, Nanjing 210
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
3277-3281
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）