Authors: Mathu Soothana S Kumar Retna Swami Muneeswaran Karuppiah
Publish Date: 2013/03/12
Volume: 28, Issue: 2, Pages: 322-328
Abstract
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 9625 9944 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
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