Abstract：Illumination and pose variations make the performance of the Locality Preserving Projections (LPP)in face recognition decrease. To solve the problem, a supervised LPP using discriminant information is presented in this paper, the proposal calls for the establishment of a feature subspace in which the intrasubject variation is minimized, while the intersubject variation is maximized, then face recognition is implemented with the subspace. Experimentation results on Havard and Umist indicate that this approach is robust to illumination and pose and has higher recognition rate than LPP and other subspace methods.
张志伟; 杨帆; 夏克文; 杨瑞霞. 一种有监督的LPP算法及其在人脸识别中的应用[J]. 电子与信息学报, 2008, 30(3): 539-541 .
Zhang Zhi-wei; Yang Fan; Xia Ke-wen; Yang Rui-xia . A Supervised LPP Algorithm and Its Application to Face Recognition. , 2008, 30(3): 539-541 .
2008, Vol. 30 
