Robust Single Sample Per Person Face Recognition with Probabilistic Illumination Enhancement

Authors

  • Muhammad Tariq Siddique School of Computing and Informatics, Universiti Teknologi Brunei, Brunei Darussalam
  • Ibrahim Venkat School of Computing and Informatics, Universiti Teknologi Brunei, Brunei Darussalam
  • Shah Hasan Shah Newaz School of Computing and Informatics, Universiti Teknologi Brunei, Brunei Darussalam
  • Sharul Tajuddin School of Computing and Informatics, Universiti Teknologi Brunei, Brunei Darussalam

DOI:

https://doi.org/10.32890/jict2025.24.3.5

Keywords:

Illumination, probabilistic image enhancement, Retinex theory, SSPP face recognition, FaceNet, SVM

Abstract

The challenges imposed by the Single Sample Per Person face recognition problem become especially critical when face images are captured in uncontrolled environments, which typically involve variations in illumination, facial expression, pose, occlusion, and more. In particular, illumination changes caused by poor lighting conditions can significantly degrade the performance of face recognition systems. In this paper, we investigate the SSPP face recognition problem in the presence of potential illumination variation and propose a two-fold approach. Firstly, we analyse and quantify the illumination variations in face images and then normalise these variations based on a probabilistic image enhancement approach. Subsequently, the feature embedding process was performed using the deep learning-based FaceNet system, and finally, faces were classified using the classical Support Vector Machine. The performance of the proposed approach is demonstrated through comprehensive experiments compared to state-of-the-art techniques. The proposed method achieved significant accuracy improvements, attaining 96.17% and 95.68% for 20 and 30 subjects, respectively, on the Extended Yale-B dataset. The performance of the proposed method is evaluated and exhibits superiority in handling illumination compared to state-of-the-art counterparts.

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31-07-2025

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Vol. 24 No. 3 (2025): Journal of Information and Communication Technology (JICT) Vol.24, No.3, July 2025

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Siddique, M. T., Venkat, I., Newaz, S. H. S., & Tajuddin, S. (2025). Robust Single Sample Per Person Face Recognition with Probabilistic Illumination Enhancement. Journal of Information and Communication Technology, 24(3), 90-112. https://doi.org/10.32890/jict2025.24.3.5

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Identifiers DOI 10.32890/jict2025.24.3.5 OpenAlex W4412815008 Scopus 105017654299