OpticalDR：一种用于隐私保护性抑郁识别的深度光学成像模型
OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition
AI总结：
本文提出OpticalDR，通过可学习光学镜头与DR及面部分析辅助任务端到端联合优化，在不可逆去除面部身份信息的同时保留抑郁相关特征，实现隐私保护且具竞争力的识别性能。
摘要：
抑郁识别（DR）面临相当大的挑战，尤其是在隐私担忧日益增长的背景下。传统的自动化DR诊断技术需要使用面部图像，这无疑会暴露患者的身份特征并带来隐私风险。为降低患者面部图像被不当披露所带来的潜在风险，我们设计了一种新型成像系统，用于擦除所采集面部图像中的身份信息，同时保留与疾病相关的特征。该系统使身份信息恢复具有不可逆性，并保留准确DR所需的关键疾病相关特征。更具体地说，我们尝试通过一个可学习镜头记录去标识化面部图像（尽可能擦除可识别特征），该镜头与后续DR任务以及一系列面部分析相关辅助任务以端到端方式联合优化。上述策略构成了我们最终的光学深度抑郁识别网络（OpticalDR）。在CelebA、AVEC 2013和AVEC 2014数据集上的实验表明，OpticalDR取得了最先进的隐私保护性能：在常用面部识别模型上的平均AUC为0.51；同时在DR上获得具有竞争力的结果，在AVEC 2013和AVEC 2014上的MAE/RMSE分别为7.53/8.48和7.89/8.82。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
