FACE IMAGE RECOGNITION METHODS AND THEIR OPTIMIZATION IN BIOMETRIC IDENTIFICATION SYSTEMS

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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