Face Recognition by Extending Elastic Bunch Graph Matching with Particle Swarm Optimization
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| Title | Face Recognition by Extending Elastic Bunch Graph Matching with Particle Swarm Optimization |
| Authors | |
| Abstract | Elastic Bunch Graph Matching is one of the well known methods proposed for face recognition. In this work, we propose several extensions to Elastic Bunch Graph Matching and its recent variant Landmark Model Matching. We used data from the FERET database for experimentations and to compare the proposed methods. We apply Particle Swarm Optimization to improve the face graph matching procedure in Elastic Bunch Graph Matching method and demonstrate its usefulness. Landmark Model Matching depends solely on Gabor wavelets for feature extraction to locate the landmarks (facial feature points). We show that improvements can be made by combining gray-level profiles with Gabor wavelet features for feature extraction. Furthermore, we achieve improved recognition rates by hybridizing Gabor wavelet with eigenface features found by Principal Component Analysis, which would provide information contained in the overall appearance of a face. We use Particle Swarm Optimization to fine tune the hybridization weights. Results of both fully automatic and partially automatic versions of all methods are presented. The best-performing method improves the recognition rate up to 22.6% and speeds up the processing time by 8 times over the Elastic Bunch Graph Matching for the fully automatic case. |
| Publisher | ACADEMY PUBLISHER |
| Date | 2009-08-01 |
| Source | Journal of Multimedia Vol 4, No 4 (2009) |
| Rights | Copyright © ACADEMY PUBLISHER - All Rights Reserved.To request permission, please check out URL: http://www.academypublisher.com/copyrightpermission.html. |