Face recognition based on opposition particle swarm optimization and support vector machine

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

One of the most recently developed face recognition technique has utilized PSO-SVM, this method lacks in the initial phase of the PSO technique. That is in PSO; initially the populations are generated in random manner. Due to this random process, the population results may also be in random. Thus, it is not certain that this method will produce precise result. Hence to avoid this drawback, a modified face recognition method is proposed in this paper. Here, a new face recognition method based on Opposition based PSO with SVM (OPSO-SVM) is introduced. To accomplish the face recognition with our proposed OPSO-SVM, initially feature extraction process is carried out on the image database. In the feature extraction process, the efficient features are extracted and then given to the SVM training and testing process. In OPSO, the populations are generated in two ways: one is random population as same as the normal PSO technique and the other is opposition population, which is based on the random population values. The optimized parameters in SVM by OPSO efficiently perform the face recognition process. Two human face databases FERET and YALE are utilized to analyze the performance of our proposed OPSO-SVM technique and also this OPSO-SVM is compared with PSO-SVM and standard SVM techniques.

Original languageEnglish
Title of host publicationIEEE ICSIPA 2013 - IEEE International Conference on Signal and Image Processing Applications
PublisherIEEE Computer Society
Pages417-424
Number of pages8
ISBN (Print)9781479902675
DOIs
Publication statusPublished - 2013
Event2013 3rd IEEE International Conference on Signal and Image Processing Applications, IEEE ICSIPA 2013 - Melaka
Duration: 8 Oct 201310 Oct 2013

Other

Other2013 3rd IEEE International Conference on Signal and Image Processing Applications, IEEE ICSIPA 2013
CityMelaka
Period8/10/1310/10/13

Fingerprint

Face recognition
Particle swarm optimization (PSO)
Support vector machines
Feature extraction
Random processes
Testing

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

Cite this

Hasan, M., Sheikh Abdullah, S. N. H., & Ali Othman, Z. (2013). Face recognition based on opposition particle swarm optimization and support vector machine. In IEEE ICSIPA 2013 - IEEE International Conference on Signal and Image Processing Applications (pp. 417-424). [6708043] IEEE Computer Society. https://doi.org/10.1109/ICSIPA.2013.6708043

Face recognition based on opposition particle swarm optimization and support vector machine. / Hasan, Mohammed; Sheikh Abdullah, Siti Norul Huda; Ali Othman, Zulaiha.

IEEE ICSIPA 2013 - IEEE International Conference on Signal and Image Processing Applications. IEEE Computer Society, 2013. p. 417-424 6708043.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hasan, M, Sheikh Abdullah, SNH & Ali Othman, Z 2013, Face recognition based on opposition particle swarm optimization and support vector machine. in IEEE ICSIPA 2013 - IEEE International Conference on Signal and Image Processing Applications., 6708043, IEEE Computer Society, pp. 417-424, 2013 3rd IEEE International Conference on Signal and Image Processing Applications, IEEE ICSIPA 2013, Melaka, 8/10/13. https://doi.org/10.1109/ICSIPA.2013.6708043
Hasan M, Sheikh Abdullah SNH, Ali Othman Z. Face recognition based on opposition particle swarm optimization and support vector machine. In IEEE ICSIPA 2013 - IEEE International Conference on Signal and Image Processing Applications. IEEE Computer Society. 2013. p. 417-424. 6708043 https://doi.org/10.1109/ICSIPA.2013.6708043
Hasan, Mohammed ; Sheikh Abdullah, Siti Norul Huda ; Ali Othman, Zulaiha. / Face recognition based on opposition particle swarm optimization and support vector machine. IEEE ICSIPA 2013 - IEEE International Conference on Signal and Image Processing Applications. IEEE Computer Society, 2013. pp. 417-424
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