Context-Assisted Low-Light Face Detection through Global and Local Image Enhancement

Current low-light face detection usually enhances images first and then detects faces. Image enhancement focuses on the global enhancement of the whole image and is suitable for the human perspective. However, face detection requires high quality of local face regions and has different optimization objectives from image enhancement task. Such mismatches between the two tasks result in inferior performance of low-light face detection. To solve these obstacles, we propose end-to-end low-light face detection through global and local enhancement. Specifically, the proposed method consists of two components, i.e., image enhancement and face detection. For image enhancement, both global and local enhancement are explored. Global enhancement utilizes illumination constraints to enhance the overall image illumination, while local enhancement employs a generative adversarial network to improve the illumination of face regions during training. Through a combination of global and local enhancement, we reduce the gap between image enhancement and face detection and optimize the two tasks jointly. Furthermore, regions of the human body may help face detection, especially when face regions are small and blurred. Therefore, we regard body region as context to assist face detection. We use the context assistance module and optimize the annotation of the body regions with the aid of human structure prior knowledge. Experiments on the Dark Face dataset demonstrate the effectiveness of the proposed method.