Depression Recognition (DR) poses a considerable challenge,especially in the context of the growing concerns surroundingprivacy. Traditional automatic diagnosis of DRtechnology necessitates the use of facial images, undoubtedlyexpose the patient identity features and poses privacyrisks. In order to mitigate the potential risks associatedwith the inappropriate disclosure of patient facial images,we design a new imaging system to erase the identity informationof captured facial images while retain diseaserelevantfeatures. It is irreversible for identity informationrecovery while preserving essential disease-related characteristicsnecessary for accurate DR. More specifically,we try to record a de-identified facial image (erasing theidentifiable features as much as possible) by a learnablelens, which is optimized in conjunction with the followingDR task as well as a range of face analysis related auxiliarytasks in an end-to-end manner. These aforementionedstrategies form our final Optical deep Depression Recognitionnetwork (OpticalDR). Experiments on CelebA, AVEC2013, and AVEC 2014 datasets demonstrate that our OpticalDRhas achieved state-of-the-art privacy protection performancewith an average AUC of 0.51 on popular facialrecognition models, and competitive results for DR withMAE/RMSE of 7.53/8.48 on AVEC 2013 and 7.89/8.82 onAVEC 2014, respectively.