Face Image De-identification Based on Feature Embedding for Privacy Protection

With the expansion of social networking services, a large number of face images have been disclosed on the Internet. Since face recognition makes it easy to collect face images of specific persons, the collected face images can be used to attack face recognition systems, such as spoofing attacks. Face image de-identification, which makes face recognition difficult without changing the appearance of the face image, is necessary for disclosing face images safely on the Internet. In this paper, we propose a face image de-identification method by embedding facial features of another person into a face image. The proposed method uses a convolutional neural network to generate a face image that can be recognized as that of another person while preserving the appearance of the face image. Through a set of experiments using a public face image dataset, we demonstrate that the proposed method preserves the appearance of face images and has high de-identification performance against unknown face recognition models compared to conventional methods.