Journal of East China Normal University(Natural Science) ›› 2023, Vol. 2023 ›› Issue (6): 49-60.doi: 10.3969/j.issn.1000-5641.2023.06.005

• Computer Science • Previous Articles     Next Articles

Towards an identity inter-relationship-consistent face de-identification method

Yifan BU1, Xiaoling WANG1, Keke HE2, Xingjian LU1, Wenxuan WANG2,*()   

  1. 1. School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
    2. School of Computer Science, Fudan University, Shanghai 200438, China
  • Received:2022-06-18 Online:2023-11-25 Published:2023-11-23
  • Contact: Wenxuan WANG


The popularity of intelligent devices such as smartphones and surveillance cameras has led to serious face privacy problems. Face de-identification is considered an effective tool for protecting face privacy by concealing identity information. However, most de-identification methods lack explicit control and controllable changes in identifying de-identified face images, resulting in de-identified images that are inapplicable to face authentication and retrieval and other identity-related tasks. Therefore, this study proposes an identity inter-relationship-consistent face de-identification task in which the identity inter-relationship between two arbitrary de-identified faces maintained the same as before de-identification. To this end, a task-driven identity inter-relationship consistent generative adversarial network is introduced to generate de-identified faces with a consistent identity inter-relationship. A rotation-based de-identifier was designed to modify the original identity features to be de-identified with identity inter-relationship consistency. In addition, identity control loss is introduced to guarantee a precise identity generation using a de-identified generator. Qualitative and quantitative results show that our method achieves improvements compared with exiting methods for de-identifying de-identified faces as well as for maintaining their identity inter-relationship consistent.

Key words: privacy protection, face de-identification, generative adversarial network, identity inter-relationship consistency

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