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Boosting Archimedes optimization algorithm using trigonometric operators based on feature selection for facial

Imène Neggaz1, Nabil Neggaz1, Hadria Fizazi1

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Summary
This summary is machine-generated.

This study introduces scAOA, a novel optimization algorithm, for accurate gender recognition using deep facial features. scAOA outperforms existing methods, enhancing computer vision applications.

Keywords:
Facial analysisGender recognitionPretrained CNNSine cosine archimedes optimization algorithm (scAOA)Trigonometric operatorsWrapper feature selection (FS)

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Facial analysis (FA) is crucial for understanding social interactions via computer vision.
  • Pre-trained Convolutional Neural Networks (CNNs) present dimensional challenges for FA tasks.
  • Optimization algorithms are vital for improving feature selection in deep learning models.

Purpose of the Study:

  • To develop and evaluate a gender recognition system using a modified Archimedes Optimization Algorithm (scAOA).
  • To leverage scAOA for selecting optimal deep features from CNN models (AlexNet & ResNet) for gender classification.
  • To compare the performance of scAOA against other state-of-the-art optimization algorithms.

Main Methods:

  • A modified Archimedes Optimization Algorithm (scAOA) was developed, incorporating sine cosine algorithm (SCA) principles to enhance convergence and avoid local optima.
  • scAOA was applied to select relevant deep features extracted by AlexNet and ResNet models.
  • The proposed system was evaluated on the Brazilian FEI and Georgia Tech Face (GT) datasets for gender recognition.

Main Results:

  • The scAOA demonstrated superior performance in gender recognition compared to AOA, SCA, ALO, SSA, GWO, SGA, GOA, and PSO.
  • The system achieved high accuracy and F-score, validated through statistical testing.
  • scAOA effectively addressed the challenge of selecting discriminative deep features for gender classification.

Conclusions:

  • The proposed scAOA-based gender recognition system offers a significant advancement in facial analysis.
  • scAOA provides an effective solution for feature selection in deep learning models for gender classification.
  • This research contributes to the field of computer vision by improving the accuracy and efficiency of gender recognition systems.