Face recognition using illuminant locality preserving projections

作     者：刘朋樟 沈庭芝 林健文 

作者机构：School of Information and ElectronicsBeijing Institute of Technology Centre for Signal ProcessingDepartment of Electronic and Information EngineeringHong Kong Polytechnic University 

出 版 物：《Journal of Beijing Institute of Technology》 (北京理工大学学报（英文版）)

年 卷 期：2011年第20卷第1期

页      面：111-116页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：Supported by the National Natural Science Foundation of China(60772066) 

主　　题：locality preserving projections ( LPP ) illuminant direction illuminant locality preser ving projections (ILPP) face recognition 

摘      要：A novel supervised manifold learning method was proposed to realize high accuracy face recognition under varying illuminant conditions. The proposed method, named illuminant locality preserving projections （ILPP）, exploited illuminant directions to alleviate the effect of illumination variations on face recognition. The face images were first projected into low dimensional subspace, Then the ILPP translated the face images along specific direction to reduce lighting variations in the face. The ILPP reduced the distance between face images of the same class, while increase the dis tance between face images of different classes. This proposed method was derived from the locality preserving projections （LPP） methods, and was designed to handle face images with various illumi nations. It preserved the face image＇ s local structure in low dimensional subspace. The ILPP meth od was compared with LPP and discriminant locality preserving projections （DLPP）, based on the YaleB face database. Experimental results showed the effectiveness of the proposed algorithm on the face recognition with various illuminations.