OpticalDR: A Deep Optical Imaging Model for Privacy-Protective
  Depression Recognition
Yuchen Pan Junjun Jiang Kui Jiang Zhihao Wu Keyuan Yu Xianming Liu
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.