Low-illumination image contrast enhancement using adaptive gamma correction and deep learning model for person identification and verification

Person verification based on face detection and face recognition is a very important research area in the field of computer vision as it provides authentication before permitting access to resources ensuring safety and security. It is a challenging task to identify and verify a person in low-illumination images, this is because the facial features of a person in a low-illumination image are not clear as the image is of poorer quality than that of an image taken with good illumination. The existing hand-crafted feature-based approaches and deep learning models for low-illumination image contrast enhancement are typically unsatisfactory in the applications of person verification either due to over-enhancing of the image or restricting the contrast of the image while dealing with light illumination. To achieve more accurate face detection and face recognition in low light images, a new approach based on adaptive gamma correction and deep learning model is proposed in this research paper. In this work, two methods: feature-based adaptive gamma correction (FAGC) and deep learning-based adaptive gamma correction (DLAGC) are proposed for contrast enhancement. The proposed approach uses the new adaptive gamma correction-based methods (FAGC, DLAGC) for the image contrast enhancement and applies deep learning models to detect and recognize the face in the enhanced image. The enhancement of the brightness difference between objects and their backgrounds achieved by the proposed adaptive gamma correction-based methods enables the deep learning model to extract the quality semantic information, which improves the accuracy of person verification. The proposed approach is evaluated on Extended Yale Face (EYF) dataset, which is a low-illumination image dataset. The proposed framework with FAGC and DLAGC for person verification achieves an improvement of 24% and 30%, respectively, on EYF dataset and 2.5% and 10%, respectively, on Specs on Faces dataset when compared to the existing techniques.