Learning Structural Similarity with EvolutionaryGAN: A New Face Deidentification Method With the explosive growth of image sources, the development of face recognition technology and the emphasis on privacy, face deidentification has become increasingly important. The purpose of face deidentification is to hide the identity of individuals in a video or image while still retaining certain facial attributes. The mainstream methods of face deidentification are mostly based on the ksame framework, which generates anonymized faces that are not diverse and have poor visual quality. This paper presents a face deidentification method that uses an improved evolutionary generative adversarial network to synthesize faces to deidentificate, using the structural similarity index and the distance between the original face and the deidentificated face for generator selection, choosing the optimal generator to enter the next round of evolution. We have proved the feasibility of the proposed method through extensive experiments, indicating that our alteration method is indeed effective.

IdentityAware CycleGAN for Face PhotoSketch Synthesis and Recognition Abstract   Face photosketch synthesis and recognition has many applications in digital entertainment and law enforcement. Recently, generative adversarial networks (GANs) based methods have significantly improved the quality of image synthesis, but they have not explicitly considered the purpose of recognition. In this paper, we first propose an IdentityAware CycleGAN (IACycleGAN) model that applies a new perceptual loss to supervise the image generation network. It improves CycleGAN on photosketch synthesis by paying more attention to the synthesis of key facial regions, such as eyes and nose, which are important for identity recognition.  Furthermore, we develop a mutual optimization procedure between the synthesis model and the recognition model, which iteratively synthesizes better images by IACycleGAN and enhances the recognition model by the triplet loss of the generated and real samples. Extensive experiments are performed on both phototosketch and sketchtophoto tasks using the widely used CUFS and CUFSF databases. The results show that the proposed method performs better than several stateoftheart methods in terms of both synthetic image quality and photosketch recognition accuracy.