Pri-EMO: A universal perturbation method for privacy preserving facial emotion recognition

Abstract

Facial emotion have great significance in human-computer interaction, virtual reality and people's communication. Existing methods for facial emotion privacy mainly concentrate on the perturbation of facial emotion images. However, cryptography-based perturbation algorithms are highly computationally expensive, and transformation-based perturbation algorithms only target specific recognition models. In this paper, we propose a universal feature vector-based privacy-preserving perturbation algorithm for facial emotion. Our method implements privacy-preserving facial emotion images on the feature space by computing tiny perturbations and adding them to the original images. In addition, the proposed algorithm can also enable expression images to be recognized as specific labels. Experiments show that the protection success rate of our method is above 95% and the image quality evaluation degrades no more than 0.003. The quantitative and qualitative results show that our proposed method has a balance between privacy and usability.