核判别保局投影的人脸识别算法

Kernel Discriminant locality preserving projections algorithm for face recognition

针对人脸识别中的特征提取问题,提出了核判别保局投影算法,即KDLPP。该算法通过核技巧将人脸样本映射到高维空间,在高维空间中有效地结合人脸局部的流形结构和人脸的判别信息构建了新的目标函数,其优点是在保持人脸流形结构的基础上,充分利用了样本的类别信息,并采用核方法提取了人脸的非线性特征。在ORL和UMIST人脸库上的实验表明,该方法的识别率整体优于LPP、DLPP和KLPP。

A new face image feature extraction and recognition algorithm based on kernel discriminant locality preserving projections is proposed,in which face images are projected into high dimensional feature spaces by using kernel trick.Then,in kernel space a new objective function is constructed with the face manifold local structure information and the labels＇ information.The advantage of the method is that the face manifold is preserved and the label information is used,at the same time,non-linear feature is abstracted.Experiments have been done on ORL and UMIST,the experimental results show that KDLPP is powerful than LPP,DLPP and KLPP.