A supervised multimanifold method with locality preserving for face recognition using single sample per person

作     者：Nabipour Mehrasa Aghagolzadeh Ali Motameni Homayun 

作者机构：Department of Computer Engineering Faculty of Engineering Sari Islamic Azad University Faculty of Electrical and Computer Engineering Babol Noshirvani University of Technology 

出 版 物：《Journal of Central South University》 (中南大学学报（英文版）)

年 卷 期：2017年第24卷第12期

页      面：2853-2861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] 

主　　题：face recognition locality preserving manifold learning single sample per person 

摘      要：Although real-world experiences show that preparing one image per person is more convenient, most of the appearance-based face recognition methods degrade or fail to work if there is only a single sample per person(SSPP). In this work, we introduce a novel supervised learning method called supervised locality preserving multimanifold(SLPMM) for face recognition with SSPP. In SLPMM, two graphs: within-manifold graph and between-manifold graph are made to represent the information inside every manifold and the information among different manifolds, respectively. SLPMM simultaneously maximizes the between-manifold scatter and minimizes the within-manifold scatter which leads to discriminant space by adopting locality preserving projection(LPP) concept. Experimental results on two widely used face databases FERET and AR face database are presented to prove the efficacy of the proposed approach.