中文摘要：

提出一种基于流形学习的特征提取方法——鉴别最大间距准则．该方法采用线性投影，保留最优的局部和全局信息数据集．试图找到具有最好鉴别能力的原始信息，使类问离散度最大的同时类内离散尽可能的小．该方法在识别率上比其它方法都有较大提高，通过在YALE和JAFFE人脸库上的实验验证该方法的有效性．

英文摘要：

A manifold learning algorithm is proposed called discriminant maximum margin criterion （DMMC）. It adopts linear projective maps and optimally preserves the local structure and the global information of the data set simultaneously. DMMC tries to find the intrinsic manifold that discriminates different face classes best by maximizing the between-class scatter and minimizing the within-class scatter. The recognition rate of the proposed algorithm exceeds those of the single PCA, Fisherfaces, MMC and LPP greatly. Experimental results on YALE and JAFFE face databases indicate that the proposed algorithm is effective.