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An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis.

Imène Neggaz1, Hadria Fizazi1

  • 1Laboratoire Signal Image PArole (SIMPA), Département d'informatique, Faculté des Mathématiques et Informatique, Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf, USTO-MB, EL M'naouer, BP 1505, 31000 Oran, Algérie.

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Summary
This summary is machine-generated.

This study introduces the Archimedes optimization algorithm (AOA) for human gender recognition from faces. The AOA method effectively identifies optimal facial regions, achieving high accuracy and outperforming deep learning models in tests.

Keywords:
Archimedes optimization algorithm (AOA)Automatic selectionHandcrafted methodsHuman facial analysis (HFA)Wrapper feature selection (FS)

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Biometric Recognition

Background:

  • Human facial analysis (HFA) is crucial for computer vision and mobile applications, encompassing tasks like gender, expression, age, and race recognition.
  • Accurate gender recognition faces challenges from variations in illumination, occlusion, facial expressions, image quality, and camera angles.
  • Existing optimization algorithms have limitations in balancing exploration and exploitation for feature selection in complex datasets.

Purpose of the Study:

  • To propose and evaluate the Archimedes optimization algorithm (AOA) as a wrapper feature selection technique for human gender recognition (HGR).
  • To automatically identify the optimal facial regions for gender classification using AOA.
  • To compare the performance of AOA-based feature selection with other optimization methods and state-of-the-art deep learning models.

Main Methods:

  • The Archimedes optimization algorithm (AOA), a metaheuristic inspired by Archimedes' principle, was employed for feature selection.
  • Facial images were divided into subregions, and feature vectors were extracted using handcrafted techniques like Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG), and Gray-Level Co-occurrence Matrix (GLCM).
  • The proposed AOA method was evaluated on benchmark datasets (Georgia Tech, FEI) and a challenging large-scale dataset (Gallagher's uncontrolled dataset).

Main Results:

  • The AOA demonstrated strong performance across all tested datasets, outperforming other competitive optimizers.
  • The AOA-based LBP approach achieved a remarkable accuracy of 96.08% on Gallagher's dataset, surpassing state-of-the-art deep convolutional neural networks (CNNs).
  • The results validate AOA's effectiveness in selecting optimal facial regions for gender recognition, even under challenging conditions.

Conclusions:

  • The Archimedes optimization algorithm (AOA) is a highly effective method for feature selection in human gender recognition tasks.
  • AOA offers a robust and efficient approach to identifying discriminative facial regions, leading to improved classification accuracy.
  • This research highlights the potential of AOA in advancing computer vision applications, particularly in sensitive biometric identification.