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Comparative Analysis of Preprocessing Techniques for Enhanced Facial Recognition under Challenging Conditions

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  • Awodeyi, Afolabi I.

    (Department of Computer Engineering, Delta State University of Science and Technology, Ozoro Nigeria)

  • Ibok, Omolegho A.

    (Department of Computer Engineering, Delta State University of Science and Technology, Ozoro Nigeria)

  • Idama, Omokaro

    (Department of Computer Engineering, Delta State University of Science and Technology, Ozoro Nigeria)

  • Ekwemuka, Jones U.

    (Department of Computer Engineering, Delta State University of Science and Technology, Ozoro Nigeria)

  • Ebem, Deborah U.

    (Department of Computer Engineering, Veritas University Abuja, Nigeria.)

  • Mamah, Rotenna O

    (Department of Computer Engineering, Veritas University Abuja, Nigeria.)

  • Ugwu, Obinna C.

    (Department of Computer Engineering, Veritas University Abuja, Nigeria.)

Abstract

Facial recognition systems often face challenges in scenarios with low-light conditions and occlusions, where critical facial features are obscured or poorly illuminated. This paper investigates the effectiveness of various preprocessing techniques, such as histogram equalization, gamma correction, and noise reduction, in enhancing facial recognition performance under such challenging conditions. Visual comparisons of images before and after preprocessing are included to demonstrate tangible improvements. Additionally, the computational efficiency and resource trade-offs of combined preprocessing techniques are analyzed, providing insights into their practical applicability. By integrating these techniques with facial recognition models, significant improvements in accuracy and feature extraction capabilities can be achieved. The methodology outlines the applied techniques and the experimental setup used for comparative analysis. Results indicate that certain preprocessing strategies, when used in combination, yield superior performance in handling difficult conditions. This study provides insights into optimizing preprocessing pipelines for robust facial recognition.

Suggested Citation

  • Awodeyi, Afolabi I. & Ibok, Omolegho A. & Idama, Omokaro & Ekwemuka, Jones U. & Ebem, Deborah U. & Mamah, Rotenna O & Ugwu, Obinna C., 2025. "Comparative Analysis of Preprocessing Techniques for Enhanced Facial Recognition under Challenging Conditions," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 12(2), pages 35-41, February.
    • Handle: RePEc:bjc:journl:v:12:y:2025:i:2:p:35-41
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    References listed on IDEAS

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    1. Valueva, M.V. & Nagornov, N.N. & Lyakhov, P.A. & Valuev, G.V. & Chervyakov, N.I., 2020. "Application of the residue number system to reduce hardware costs of the convolutional neural network implementation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 177(C), pages 232-243.
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