ENHANCING FACIAL RECOGNITION ACCURACY IN LOWLIGHT ENVIRONMENTS USING NEURAL NETWORKS

Abstract

Facial recognition technology has become a cornerstone in various applications, ranging from personal device authentication to advanced surveillance systems. However, maintaining accuracy under low-light conditions remains a critical challenge. This study explores innovative neural network techniques, such as the Deep Retinex Decomposition Network (DRDN), CenterFace, and RetinaFace, to enhance recognition accuracy in low-light scenarios. By leveraging datasets like DARKFACE and LOL, this research demonstrates how state-of-the-art image enhancement, feature fusion, and detection algorithms can mitigate the challenges of poor lighting and feature obscuration. Detailed implementation strategies, including dataset preparation, preprocessing, and hybrid model architectures, are discussed. Experimental results show significant improvements in recognition accuracy, noise reduction, and computational efficiency, paving the way for more reliable and versatile facial recognition systems across applications such as security, healthcare, and consumer electronics.