Recoverable Anonymization for Pose Estimation: A Privacy-Enhancing Approach

Wenjun Huang, Yang Ni, Arghavan Rezvani Dehaghani, SungHeon Evan Jeong, Hanning Chen, Yezi Liu, Fei Wen, Mohsen Imani; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 5239-5249

Human pose estimation (HPE) is crucial for various applications. However deploying HPE algorithms in surveillance contexts raises significant privacy concerns due to the potential leakage of sensitive personal information (SPI) such as facial features and ethnicity. Existing privacy-enhancing methods often compromise either privacy or performance or they require costly additional modalities. We propose a novel privacy-enhancing system that generates privacy-enhanced portraits while maintaining high HPE performance. Our key innovations include the reversible recovery of SPI for authorized personnel and the preservation of contextual information. By jointly optimizing a privacy-enhancing module a privacy recovery module and a pose estimator our system ensures robust privacy protection efficient SPI recovery and high-performance HPE. Experimental results demonstrate the system's robust performance in privacy enhancement SPI recovery and HPE. The code associated with this study can be found at this URL.