Selection algorithm of Gabor Kernel for Face Recognition
Because the fact that Gabor feature are redundant and too high-dimensional, appropriate feature dimension reduction appears to be much more necessary. To address this problem, a novel optimal selection method of Gabor kernels’ scales and orientation is proposed. In this method, all training samples are convolved with each Gabor kernel. Within-class distance and between-class distance calculation are performed on these convolution results, respectively. At last, the optimal Gabor kernel is selected based on the ratio of the Within-class distance and the between-class distance. The Gabor Kernel corresponding to the largest ratio is the optimal one. To prove the advantages of proposed method, extensive experiments are conducted on popular face databases such as YALE, AR, FERET. The experiment results shows that the proposed method is effective and the features in the larger scales as well as the features in several orientations have more discriminative power.
Face recognition Gabor kernel dimension reduction
LI Xiao-Dong Yuan Wei
School of Logistics, Linyi University, Linyi 276005, China School of Information, Linyi University, Linyi 276005, China
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
3839-3843
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）