An enhancement of the Eigenface algorithm using weber local descriptor applied in attendance management system

Amyr Edmar Francisco, Angelo Lance Seraspi, Jamillah Guialil, & Khatalyn Mata

Volume 2 Issue 1 May 2025

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https://doi.org/10.53378/isrr.164

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Abstract

This study presents an improved face recognition system tackling the Eigenface algorithm's limitations regarding lighting variance, class separability, and classification. The proposed method incorporates Weber Local Descriptor (WLD) for illumination normalization during training and recognition. Further improvements include Kernel Principal Component Analysis (KPCA) for non-linear feature transformation, Linear Discriminant Analysis (LDA) to maximize class separability, and Ridge classification for noise-resistant recognition, replacing Euclidean distance. Testing on the extended Yale B dataset showed a significant accuracy increase from 5.63% (original Eigenface) to 99.83% (enhanced Eigenface). Evaluation on a custom dataset simulating real-world conditions (varying light, expressions) yielded 100% accuracy across feature transformation, class separability, and classification. These results demonstrate the effectiveness of the integrated WLD, KPCA, LDA, and Ridge classification techniques in developing a robust and accurate face recognition system suitable for applications like attendance management.

Keywords

Author information & Contribution

Amyr Edmar Francisco. Corresponding author. 4th year Computer Science student at Pamantasan ng Lungsod ng Maynila and former GDSC-PLM CTO with interests in data science and machine learning. Email: amyrfrancisco11@gmail.com

Angelo Lance Seraspi. 4th year Computer Science student at Pamantasan ng Lungsod ng Maynila focused on full-stack development and AI.

Jamillah Guialil. Professor in Computer Science at Pamantasan ng Lungsod ng Maynila.

Khatalyn Mata. Dean of College of Information Systems and Technology Management at Pamantasan ng Lungsod ng Maynila.

"Amyr Edmar L. Francisco developed the code for the enhanced Eigenface algorithm and implemented the attendance system software. Angelo Lance O. Seraspi conducted the literature review and contributed significantly to writing the main content of the paper. Jamillah S. Guialil provided domain-specific guidance and expert advice throughout the research process. Dr. Khatalyn E. Mata offered additional advisory support, reviewed the revisions, and approved the final version of the paper."

Disclosure statement

Funding

AI Declaration

Notes

Acknowledgement

We would like to express our sincerest gratitude to our esteemed professors, Prof. Jamillah S. Guialil and Dr. Khatalyn E. Mata, for their invaluable guidance, unwavering support, and insightful feedback throughout this research endeavor. Their expertise and encouragement were instrumental in shaping the direction and ensuring the successful completion of this paper.
Our heartfelt appreciation also goes to our distinguished panelists, Mr. Meggy Ortiz and Mr. Michael Joseph Maquilan, for dedicating their time and sharing their valuable insights and constructive criticism during the defense. Their feedback significantly contributed to the refinement and improvement of our work.
We extend our deepest thanks to our friends and family for their unwavering love, understanding, and encouragement throughout this challenging yet rewarding journey. Their moral support and belief in us provided the motivation needed to persevere.
Finally, and most importantly, we offer our profound gratitude to the Lord for His constant grace, wisdom, and blessings that have guided us throughout this entire process.

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Cite this article:

Francisco, A.E., Seraspi, A.L., Guialil, J. & Mata, K. (2025). An enhancement of the Eigenface algorithm using weber local descriptor applied in attendance management system. International Student Research Review, 2(1), 139-173. https://doi.org/10.53378/isrr.164