Support vector machine based on adaptive acceleration particle swarm optimization

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.

Original languageEnglish
Article number835607
JournalThe Scientific World Journal
Volume2014
DOIs
Publication statusPublished - 2014

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Particle swarm optimization (PSO)
Support vector machines
Iris
Support Vector Machine
support vector machine
particle
Testing
Face recognition
Feature extraction
Databases
fitness
Facial Recognition

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Environmental Science(all)
  • Medicine(all)

Cite this

Support vector machine based on adaptive acceleration particle swarm optimization. / Abdulameer, Mohammed Hasan; Sheikh Abdullah, Siti Norul Huda; Ali Othman, Zulaiha.

In: The Scientific World Journal, Vol. 2014, 835607, 2014.

Research output: Contribution to journalArticle

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