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

Yuchen Pan, Junjun Jiang, Kui Jiang, Zhihao Wu, Keyuan Yu, Xianming Liu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 1303-1312

Depression Recognition (DR) poses a considerable challenge especially in the context of the growing concerns surrounding privacy. Traditional automatic diagnosis of DR technology necessitates the use of facial images undoubtedly expose the patient identity features and poses privacy risks. In order to mitigate the potential risks associated with the inappropriate disclosure of patient facial images we design a new imaging system to erase the identity information of captured facial images while retain 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 analysis related auxiliary tasks in an end-to-end manner. These aforementioned strategies form our final Optical deep Depression 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 MAE/RMSE of 7.53/8.48 on AVEC 2013 and 7.89/8.82 on AVEC 2014 respectively. Code is available at https://github.com/divertingPan/OpticalDR.