Feature selection for face recognition:a memetic algorithmic approach

作     者：Dinesh KUMAR Shakti KUMAR C.S.RAI 

作者机构：Department of Computer Science & EngineeringGuru Jambheshwar University of Science & Technology Institute of Science and Technology University School of Information TechnologyGGS Indraprastha University 

出 版 物：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 (浙江大学学报（英文版）A辑（应用物理与工程）)

年 卷 期：2009年第10卷第8期

页      面：1140-1152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] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Face recognition Memetic algorithm (MA) Principal component analysis (PCA) Linear discriminant analysis (LDA) Kernel principal component analysis (KPCA) Feature selection 

摘      要：The eigenface method that uses principal component analysis(PCA) has been the standard and popular method used in face *** paper presents a PCA-memetic algorithm(PCA-MA) approach for feature *** has been extended by MAs where the former was used for feature extraction/dimensionality reduction and the latter exploited for feature *** were performed over ORL and YaleB face databases using Euclidean norm as the *** was found that as far as the recognition rate is concerned,PCA-MA completely outperforms the eigenface *** compared the performance of PCA extended with genetic algorithm(PCA-GA) with our proposed PCA-MA *** results also clearly established the supremacy of the PCA-MA method over the PCA-GA *** further extended linear discriminant analysis(LDA) and kernel principal component analysis(KPCA) approaches with the MA and observed significant improvement in recognition rate with fewer *** paper also compares the performance of PCA-MA,LDA-MA and KPCA-MA approaches.