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Updated: Apr 30, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Support vector machine based on adaptive acceleration particle swarm optimization.

Mohammed Hasan Abdulameer1, Siti Norul Huda Sheikh Abdullah2, Zulaiha Ali Othman3

  • 1Pattern Recognition Research Group, Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bandar Baru Bangi, Malaysia ; Department of Computer Science, Faculty of Education for Women, University of Kufa, Iraq.

Thescientificworldjournal
|May 3, 2014
PubMed
Summary

A new Adaptive Acceleration Particle Swarm Optimization (AAPSO) method improves face and iris recognition by optimizing Support Vector Machine (SVM) parameters. This technique enhances accuracy by using particle fitness values, outperforming traditional PSO methods.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • Particle Swarm Optimization (PSO) and Opposition-based PSO (OPSO) are used to optimize Support Vector Machine (SVM) parameters for face recognition.
  • Random values in velocity calculation during PSO/OPSO decrease the performance of these optimization techniques.

Purpose of the Study:

  • To propose an Adaptive Acceleration Particle Swarm Optimization (AAPSO) technique to address the limitations of random value utilization in PSO/OPSO.
  • To evaluate the performance of AAPSO in conjunction with SVM (AAPSO-SVM) for face and iris recognition.

Main Methods:

  • Feature extraction followed by recognition using extracted features.
  • Support Vector Machine (SVM) training and testing using extracted features.
  • Optimization of SVM parameters using AAPSO, where acceleration coefficients are computed based on particle fitness values.

Main Results:

  • The proposed AAPSO-SVM method demonstrates efficient performance in both face and iris recognition tasks.
  • AAPSO optimizes SVM parameters effectively, leading to improved recognition accuracy compared to traditional PSO-SVM.

Conclusions:

  • AAPSO offers an effective approach for optimizing SVM parameters in biometric recognition systems.
  • The proposed AAPSO-SVM technique shows superior performance for face and iris recognition compared to existing PSO-SVM methods.