IMPROVING DETECTION AND RECOGNITION OF DEGRADED FACES BY DISCRIMINATIVE FEATURE RESTORATION USING GAN

Face detection and recognition in the wild is currently one of the most interesting and challenging problems. Many algorithms with high performance have already been proposed and applied in real-world applications. However, the problem of detecting and recognising degraded faces from low-quality images and videos mostly remains unsolved. In this paper, we present an algorithm capable of recovering facial features from very low quality videos and images. The resulting output image boosts the performance of existing face detection and recognition algorithms. It contains an effective method involving metric learning and different loss function components operating on different parts of the generator. This enhances the degraded faces by restoring their lost features rather than its perceptual quality. Our approach has been experimentally proven to enhance face detection and recognition, e.g., the face detection rate is improved by 3.08% for S3FD and the area under the ROC curve for recognition is improved by 2.55% for ArcFace on the SCFace dataset.