SeLENet: A Semi-Supervised Low Light Face Enhancement Method for Mobile Face Unlock
Ha An Le, Ioannis A. Kakadiaris · 2019
Facial recognition is becoming a standard feature on new smartphones. However, the face unlocking feature of devices using regular 2D camera sensors exhibits poor performance in low light environments. In this paper, we propose a semi-supervised low light face enhancement method to improve face verification performance on low light face images. The proposed method is a network with two components: decomposition and reconstruction. The decomposition component splits an input low light face image into face normals and face albedo, while the reconstruction component enhances and reconstructs the lighting condition of the input image using the spherical harmonic lighting coefficients of a direct ambient white light. The network is trained in a semi-supervised manner using both labeled synthetic data and unlabeled real data. Qualitative results demonstrate that the proposed method produces more realistic images than the state-of-the-art low light enhancement algorithms. Quantitative experiments confirm the effectiveness of our low light face enhancement method for face verification. By applying the proposed method, the gap of verification accuracy between extreme low light and neutral light face images is reduced from approximately 3% to 0.5%.