OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition

Abstract

Depression Recognition (DR) poses a considerable chal-lenge, especially in the context of the growing concerns surrounding privacy. Traditional automatic diagnosis of DR technology necessitates the use of facial images, un-doubtedly expose the patient identity features and poses privacy risks. In order to mitigate the potential risks as-sociated with the inappropriate disclosure of patient fa-cial images, we design a new imaging system to erase the identity information of captured facial images while re-tain disease-relevant features. It is irreversible for identity information recovery while preserving essential disease-related characteristics necessary for accurate DR. More specifically, we try to record a de-identified facial image (erasing the identifiable features as much as possible) by a learnable lens, which is optimized in conjunction with the following DR task as well as a range of face analy-sis related auxiliary tasks in an end-to-end manner. These aforementioned strategies form our final Optical deep De-pression Recognition network (OpticalDR). Experiments on CelebA, AVEC 2013, and AVEC 2014 datasets demonstrate that our OpticalDR has achieved state-of-the-art privacy protection performance with an average AUC of 0.51 on popular facial recognition models, and competitive results for DR with MAEIRMSE of 7.5318.48 on AVEC 2013 and 7.8918.82 on AVEC 2014, respectively. Code is available at https://github.com/divertingPanIOpticalDR.