PrivacyHFR: Visual Privacy Preserving for Heterogeneous Face Recognition

Face recognition has achieved remarkable progress and is widely deployed in real-world scenarios. Recently more and more attention has been given to individual privacy protection, due to unauthorized sensitive image leakage by malicious attackers. Multi-modality face images captured by diverse sensors, also called heterogeneous faces, bring in more challenges in face privacy protection while lacking related research. In this paper, we propose a novel visual Privacy preserving method for Heterogeneous Face Recognition (Privacy-HFR) to protect perceptual visual information and maintain essential identity information in multi-modality face analysis scenarios. Frequency domain analysis is a vital strategy to bridge the inevitable modality gap for heterogeneous face images. Meanwhile, recent theoretical insights also inspire us to design a suitable frequency component adjustment to balance human visual sensitivity and identity discriminative information. In addition, the ability to defend against recovery attacks has emerged as an essential criterion for privacy preserving face recognition. Noting that there seems to exist a dilemma that reducing accessible information by the attack model will affect the extracted identity information for recognition. It is because these two kinds of information are mutually blended in the frequency domain, which makes it a challenge to simultaneously maintain visual privacy and identity distinguishability. Thus, we provide a novel perspective to leverage the randomly optimal solutions and design the specific adversarial perturbations against the recovery attack. Experiments on several large-scale heterogeneous face datasets (CASIA NIR-VIS 2.0, LAMP-HQ, Tufts Face and CUFSF datasets) prove that the proposed method outperforms existing privacy-preserving face recognition methods in terms of recognition accuracy and privacy protection capability. The code is available in https://github.com/xiyin11/Privacy-HFR.