Brightness and Contrast Adaptive Face Recognition System

Recently, artificial intelligence has made remarkable advances and is being extensively utilized in many fields. However, there are challenges in adapting to real-world conditions in the field of face recognition where even minor errors are not tolerated. To address this, we propose a face recognition system that can remove brightness and contrast from images, which are common real-world conditions, without retraining the model. Our system can adapt to brightness and contrast accordingly using a brightness and contrast removal algorithm. The algorithm generates nearly identical images for comparison by removing brightness and contrast. Experimental results demonstrate high face recognition accuracy compared to existing systems, improving true positive and false negative accuracy by 4.55% and 10.34%, respectively, with a minimal computing overhead of only 1.40%. Overall, the face recognition accuracy improved by 4.60%.