Reversible Privacy-Preserving Recognition

Zhengxin You, Sheng Li, Zhenxing Qian, Xinpeng Zhang

Abstract: In this paper, we propose a novel reversible face privacy-preserving scheme. Before uploading facial images onto the cloud, we first cover the facial region with mosaic and train an encoder to generate protected images with original facial information embedded. We train another classifier with protected images for facial expression recognition and a decoder for recovering original facial images. On the cloud service, protected images provide little identity information to malicious attackers. For low-privileged users, they can use the provided classifier to do computer vision tasks with protected images. For authorized users, after content recovery, the nor-mal usage of the facial images will not be affected. Experimental results show that the proposed method is effective in facial images recovery. In addition, the protected images can maintain similar accuracy on typical computer vision tasks compared to the original images.