Optimal Feature Extraction Using Greedy Approach for Random Image Components and Subspace Approach in Face Recognition

作     者：Mathu Soothana S.Kumar Retna Swami Muneeswaran Karuppiah 

作者机构：Department of Information Technology Noorul Islam University Department of Computer Science and Engineering Mepco Schlenk Engineering College 

出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)

年 卷 期：2013年第28卷第2期

页      面：322-328页

核心收录：

学科分类：0808[工学-电气工程] 07[理学] 08[工学] 080203[工学-机械设计及理论] 070104[理学-应用数学] 0835[工学-软件工程] 0802[工学-机械工程] 081101[工学-控制理论与控制工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：face recognition multiple discriminant analysis optimal random image component selection principal com- ponent analysis recognition accuracy 

摘      要：An innovative and uniform framework based on a combination of Gabor wavelets with principal component analysis （PCA） and multiple discriminant analysis （MDA） is presented in this paper. In this framework, features are extracted from the optimal random image components using greedy approach. These feature vectors are then projected to subspaces for dimensionality reduction which is used for solving linear problems. The design of Gabor filters, PCA and MDA are crucial processes used for facial feature extraction. The FERET, ORL and YALE face databases are used to generate the results. Experiments show that optimal random image component selection （ORICS） plus MDA outperforms ORICS and subspace projection approach such as ORICS plus PCA. Our method achieves 96.25%, 99.44% and 100% recognition accuracy on the FERET, ORL and YALE databases for 30% training respectively. This is a considerably improved performance compared with other standard methodologies described in the literature.