采用PCA/ICA特征和SVM分类的人脸识别

人脸识别过程中，首先在主成分分析基础上进一步做独立成分分析，来提取更加有利于分类的面部特征的主要独立成分；然后采用一种分阶段淘汰的支持向量机分类机制进行识别．该方法扩展了支持向量机处理多类问题的能力，它基于1-1差别策略，根据各判别函数VC置信范围的差异进行排序，同时利用判别函数间的冗余来降低识别误差．对两组人脸图像库的测试结果表明，文中方法在识别率和识别时间等方面都取得了较好的效果。

Face Recognition Based on Features by PCA/ICA and Classification with SVM

This paper deals with combined approach of principle component analysis (PCA) and independent component analysis (ICA) to get the representation basis of face image set and proposes a new multi-step approach to extend support vector machine (SVM) capability to deal with multi-class face recognition by incorporating with the elimination strategy. Based on the one-against-one classifying strategy, it sorts the discrimination functions according to their own Vapnik-Chervonenkis confidence and uses the redundancy among them to decrease the discrimination error in case of rejecting decision. Experiments with two face-databases show that the proposed method has reached a higher recognition rate with reasonable time cost.