Face Anonymity Based on Facial Pose Consistency

With the development of artificial intelligence, there are more applications related to face images. The recording of face information causes potential cyber security risks and personal privacy disclosure risks to the public. To solve this problem, the authors hope to protect face privacy through face anonymity. This paper designs a conditional autoencoder that uses the data preprocessing method of image inpainting. Based on the realistic generation ability of StyleGAN, their autoencoder model introduces facial pose information as conditional information. The input image only contains preprocessed face-removed images. The method can generate high-resolution images and maintain the posture of the original face. It can be used for identity-independent computer vision tasks. Experiments further prove the effectiveness of the anonymization framework.