SeLENet: A SemiSupervised Low Light Face Enhancement Method for Mobile Face Unlock 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 semisupervised 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 semisupervised manner using both labeled synthetic data and unlabeled real data. Qualitative results demonstrate that the proposed method produces more realistic images than the stateoftheart 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%.

Extreme LowLight Image Enhancement for Surveillance Cameras Using Attention UNet Lowlight image enhancement is one of the most challenging tasks in computer vision, and it is actively researched and used to solve various problems. Most of the time, image processing achieves significant performance under normal lighting conditions. However, under lowlight conditions, an image turns out to be noisy and dark, which makes subsequent computer vision tasks difficult. To make buried details more visible, and reduce blur and noise in a lowlight captured image, a lowlight image enhancement task is necessary. A lot of research has been applied to many different techniques. However, most of these approaches require much effort or expensive equipment to perform lowlight image enhancement. For example, the image has to be captured in a raw camera file in order to be processed, and the addressing method does not perform well under extreme lowlight conditions. In this paper, we propose a new convolutional network, Attention Unet (the integration of an attention gate and a Unet network), which is able to work on common file types (.PNG, .JPEG, .JPG, etc.) with primary support from deep learning to solve the problem of surveillance camera security in smart city inducements without requiring the raw image file from the camera, and it can perform under the most extreme lowlight conditions.