Vol. 1 No. 5 (2025), Articles
Vol. 1 No. 5 (2025)

FACE IMAGE RECOGNITION METHODS AND THEIR OPTIMIZATION IN BIOMETRIC IDENTIFICATION SYSTEMS

Articles
Published 2025-04-21
Mukhamadiev Abdivali Shukurovich
Doctor of Physics and Mathematics, Professor of the Department of “Television and Media Technologies”, TUIT Uzbekistan, Tashkent
Feruza Sodik kizi Ortikova
PhD student, TUIT Uzbekistan, Tashkent
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Keywords

Face recognition, biometric identification, machine learning, neural networks, feature extraction, optimization, deep learning, GANs, CNNs, transfer learning, biometrics, face authentication, lighting variability, pose invariance, surveillance systems, computational complexity, resource optimization, ethical considerations, hybrid models, bias mitigation, data privacy.

Abstract

Face recognition is a key biometric identification technology, providing secure authentication in applications like surveillance, border control, and mobile security. Despite advances, challenges such as lighting variability, pose changes, and computational complexity require continuous optimization. This study reviews face recognition techniques, from traditional methods like Principal Component Analysis (PCA] and Linear Discriminant Analysis (LDA] to deep learning-based approaches, including Convolutional Neural Networks (CNNs] and Generative Adversarial Networks (GANs].

Optimization strategies, such as feature extraction, hyperparameter tuning, dataset augmentation, and transfer learning, significantly enhance accuracy and efficiency. CNN-based models outperform traditional methods, achieving over 99% accuracy under controlled conditions. GANs improve data augmentation, enhancing performance in diverse scenarios. Edge optimization enables real-time deployment on resource-limited devices.

Balancing accuracy, computational efficiency, and ethical concerns, including bias mitigation and data privacy, is crucial. Future research should integrate traditional and deep learning models for optimized performance. Ongoing interdisciplinary collaboration is vital for secure and fair biometric identification systems.

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