Super-resolving blurry face images with identity preservation

Abstract Face images captured in unconstrained settings may suffer from one or multiple degradations, which would degrade the visual aesthetics of images and the performance of face recognition methods. However, many current methods only focus on a specific degradation or restoring the images without considering face identity. To address these problems, an identity-preservation-based deep learning method is proposed for super-resolving blurry face images. First, an extra recognition module is designed and integrated with the restoration module to extract different levels of identity-related and semantic features. Second, an assemble loss function is developed to use the identity preservation information as regularization and prior to guide the restoration and recognition process. Finally, qualitative and quantitative evaluations are conducted to demonstrate the effectiveness of the proposed method for face recovery and face recognition. The results indicate that facial identity can serve as an effective prior to face image restoration.