Enhancing Facial Recognition Accuracy in Low-Light Conditions Using Convolutional Neural Networks

Abstract

Facial recognition technology has become increasingly everywhere in various domains, from security and surveillance to personal device authentication. However, its effectiveness can be significantly hindered in low-light conditions, where images often lack sufficient illumination for accurate recognition. This study proposes a novel approach to enhance facial recognition accuracy in low-light conditions using Convolutional Neural Networks (CNNs), Deep Retinex Decomposition Network (DRDN), and CenterFace algorithm. The methodology leverages CNNs for robust feature extraction, while DRDN corrects illumination by decomposing images. CenterFace integrates feature fusion and denoising layers for discriminative facial features and noise mitigation. Experimental results demonstrate a remarkable improvement in recognition performance, exceeding 80% accuracy. This approach showcases the potential of CNN-based methods with advanced techniques to enhance reliability in real-world facial recognition applications, particularly in low-light environments.