SF-GAN: Face De-Identification Method Without Losing Facial Attribute Information
Yongxiang Li, Qianwen Lu, Qingchuan Tao, Xingbo Zhao, Yanmei Yu
Abstract
This paper discusses the face de-identification without losing facial attribute information and provides a solution called SF-GAN (Secret Face Generative Adversarial Network). The proposed model aims to realize face de-identification effectively and generate visually reasonable images while retaining the facial attribute information of original images, such as facial expression, gender, hairstyle and wearing glasses or not, as much as possible. To optimize the face de-identification, we construct a variety of external mechanisms to balance the influence of multiple factors on the effect of face de-identification. The SF-GAN differs from the face replacement and face swapping because the later two fails actually in the privacy protection. As for the multi-attribute retention, we use the shallow face attribute information and deep face attribute information, and adopt different processing strategies for different face attribute information based on the uniqueness of each kind of face attribute information. Finally, we train and test the model on two high-definition datasets Celeba-HQ and FFHD. Compared with existing face de-identification methods, the proposed model proved to perform better in protecting the facial privacy.