An improving method for face recognition based on kernel maximum margin criterion
1. School of Information Engineering, Lanzhou Commercial College, Lanzhou, Gansu 730020, China; 2. School of Information Science and Technology, Nanjing Forestry University, Nanjing, Jiangsu 210037, China）
Abstract：A new feature extraction method based on kernel maximum margin criterion （KMMC） Was presented for nonlinear feature extraction which is a simple algorithm of statistically uncorrelated optimal discriminant vectors in kernel feature space. Compared with the original KMMC feature extraction method, the proposed method is powerful in eliminating the statistical correlation between feature vectors and improving efficiency of feature extraction in the high dimensional feature space. The experimental resuits on Olivetti Research Laboratory（ORL） face database and YALE face database show that the new method is better than original KMMC and kernel principal component analysis （KPCA） in terms of efficiency and stability about feature extraction.
李国栋, 李勇智. 一种基于核最大间距准则改进的特征提取方法[J]. 江苏大学学报（自然科学版）, 2008, 29(5): 441-444.
Li Guodong, Li Yongzhi. An improving method for face recognition based on kernel maximum margin criterion[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2008, 29(5): 441-444.
2008, Vol. 29 
