Face Anonymization Made Simple

Han-Wei Kung, Tuomas Varanka, Sanjay Saha, Terence Sim, Nicu Sebe; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 1040-1050

Current face anonymization techniques often depend on identity loss calculated by face recognition models which can be inaccurate and unreliable. Additionally many methods require supplementary data such as facial landmarks and masks to guide the synthesis process. In contrast our approach uses diffusion models with only a reconstruction loss eliminating the need for facial landmarks or masks while still producing images with intricate fine-grained details. We validated our results on two public benchmarks through both quantitative and qualitative evaluations. Our model achieves state-of-the-art performance in three key areas: identity anonymization facial attribute preservation and image quality. Beyond its primary function of anonymization our model can also perform face swapping tasks by incorporating an additional facial image as input demonstrating its versatility and potential for diverse applications. Our code and models are available at https://github.com/hanweikung/face_anon_simple.