Effective Facial Obstructions Removal with Enhanced Cycle-Consistent Generative Adversarial Networks

Face recognition has becoming an important and popular authentication technology for web services and mobile applications in recent years. The quality of facial obstruction removal is a critical component of face recognition, especially for mission critical applications such as facial recognition based authentication systems. It is well known that some facial obstructions may severely affect the extraction and recognition quality and accuracy of facial features, which in turn disturbs the prediction accuracy of facial recognition model and algorithms. In this paper, we propose a Facial Obstructions Removal Scheme (FORS) based on an Enhanced Cycle-Consistent Generative Adversarial Networks (ECGAN) for face recognition. By training a convolution neural network based facial image classifier, we identify those images that contain facial obstructions. Then the images with facial obstructions are processed by using the facial image converter of FORS and the ECGAN model, which removes facial obstructions seamlessly while preserving the facial features. Our experimental results show that the proposed FORS scheme improves the face recognition accuracy over some existing state of art approaches.