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Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform

Sulayman Ahmed1, Mondher Frikha1, Taha Darwassh Hanawy Hussein2

  • 1ENETCOM, Universite de Sfax, Tunisia.

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|March 29, 2021
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Summary
This summary is machine-generated.

This study introduces a novel face recognition system using Gabor wavelet transform and deep learning for symmetry face databases. The approach significantly enhances accuracy, achieving up to 96.22% on the ORL database.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Image Processing

Background:

  • Face recognition systems are crucial for security and identification.
  • Traditional methods often struggle with variations in lighting, pose, and expression.
  • Symmetry face databases present unique challenges for feature extraction and recognition.

Purpose of the Study:

  • To develop and evaluate a novel face recognition system for symmetry face databases.
  • To leverage the strengths of Gabor wavelet transform and deep learning for improved accuracy.
  • To assess the impact of particle swarm optimization (PSO) on feature selection for face recognition.

Main Methods:

  • Feature extraction using Gabor wavelet transform on symmetry face training data.
  • Face recognition utilizing a deep learning approach.
  • Implementation and evaluation on ORL and YALE face databases using MATLAB 2020a.
  • Comparative analysis with and without particle swarm optimization (PSO) for feature selection.

Main Results:

  • Gabor wavelet feature extraction with ample training data proved highly effective.
  • The proposed method achieved recognition rates of 85.42% (ORL) and 92% (YALE) without PSO.
  • Particle swarm optimization (PSO) improved accuracy to 96.22% (ORL) and 94.66% (YALE).

Conclusions:

  • The combination of Gabor wavelet transform and deep learning offers a robust solution for face recognition.
  • Particle swarm optimization significantly enhances the accuracy of the proposed face recognition system.
  • The developed system demonstrates high effectiveness on standard face databases like ORL and YALE.