人脸识别是模式识别领域中的一项重要的研究课题。到目前为止，已经提出了许多方法来处理人脸的识别问题。最近，许多流形学习算法被提出并且成功地应用于人脸识别当中。这些流形学习方法能够保持人脸图像数据的局部结构，同时，还可以发现人脸的非线性结构。在这些流形学习方法中，局部保持投影方法(LPP)是最有效的方法之一。基于LPP方法，提出了一种新的人脸识别方法——基于Schur分解的正交鉴别局部保持投影方法(ODLPPS)。与LPP方法相比，ODLPPS 把类间散度与类内散度之差的信息融入到LPP的目标函数中并且获得了正交的基向量。在ORL和Yale 人脸数据库上的实验结果表明，该方法在识别性能上优于一些已经存在的方法，如eigenface，Fisherface，LPP 和orthogonal LPP(OLPP)。

Face recognition is one of the hottest research areas in pattern recognition.Many face recognition methods have been proposed.Recently，a lot of learning algorithms have been proposed and applied it in face recognition tasks successfully.Among them，locality preserving projections (LPP) is one of the most effective methods.In this paper，we propose a new face recognition method——orthogonal discriminant locality preserving projections with Schur decomposition (ODLPPS).In comparison with LPP，the objective function of the proposed method incorporates scatter difference information of between-class and within-class and makes the basic vectors orthogonal.Experimental results on ORL and Yale demonstrate the proposed algorithm achieves better face recognition performance than some existing methods such as eigenface,Fisherface,LPP and orthogonal LPP(OLPP).