›› 2013, Vol. 28 ›› Issue (2): 322-328.doi: 10.1007/s11390-013-1333-5

Special Issue: Artificial Intelligence and Pattern Recognition

• Artificial Intelligence and Pattern Recognition • Previous Articles     Next Articles

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

Mathu Soothana S. Kumar Retna Swami1 and Muneeswaran Karuppiah2   

  1. 1 Department of Information Technology, Noorul Islam University, Thuckalay 629180, India;
    2 Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi 626005, India
  • Received:2012-01-22 Revised:2013-01-11 Online:2013-03-05 Published:2013-03-05

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.

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