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

查看全文 作　　者：Mathu Soothana S.Kumar Retna [1]Swami;Muneeswaran [2]Karuppiah

机构地区：[1]Department of Information Technology, Noorul Islam University;[2]Department of Computer Science and Engineering, Mepco Schlenk Engineering College

出　　处：《Journal of Computer Science & Technology》索引2013年第28卷第2期,共7页

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. 关键词：人脸特征提取子空间方法图像组件人脸识别贪心方法随机 GABOR滤波器图片

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

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