Face De-Identification for Research and Clinical Use

描述（由申请人提供）：该应用程序解决了NIH促进数据共享和患者隐私的呼吁。分享录制视频的一个主要障碍是需要保护参与者的身份。同样，对耻辱感的担忧也是许多需要心理健康服务的人（例如，在军事上，他不能这样做。我们提出了一个系统去识别视频中的患者和研究参与者。人脸去识别技术将人脸表情信息从保密的源人脸图像自动转换到不保密的目标人脸图像。该系统保护面部匿名性，同时保留原始源视频的面部表情。然后，目标视频可以传达源人的情感、交流意图、疼痛和神经或生理状态，而不显示源人的面部。面部去识别将使研究人员和临床医生之间的视频档案共享，而不会损害隐私或机密性。此外，该系统的一个版本可能被用于在基于互联网的采访中保护隐私和匿名。创新该项目有四项创新。该方法（1）在保留面部动态的同时保留身份信息;因此保留了面部的信息价值，以传达情感，疼痛和相关状态。(2)模仿微妙和自发的面部动作，而不是仅仅模仿一些预定义的磨牙表情（例如，快乐或悲伤）。(3)不需要目标人员的培训步骤。(4)并且不需要对视频进行手动注释。该系统是完全自动的。Approach.该软件将采用具有主体（源）的面部的视频作为输入，并自动生成或输出具有去识别面部的视频。该项目将使用新的机器学习和计算机视觉算法，仅使用目标对象的一张正面图像，将微妙的面部表情从源对象（原始视频）转移到目标对象。该方法的一个主要新奇是使该过程完全自动化。该算法将使用商业可用的面部识别软件和面部表情分析定制软件进行验证。

DESCRIPTION (provided by applicant): This application addresses NIH's call to promote data sharing and patient privacy. A major obstacle to sharing of recorded video has been the need to protect participants' identity. Similarly, concern about stigma is a reason that many people in need of mental health services (e.g., in the military) fail to do so. We propose a system to de-identify patients and research participants in video. Face de-identification transfers facial expression automatically from source face images, which are confidential, to target face images, which are not. The system safeguards face anonymity while preserving the facial expression of the original source video. The target video then can communicate the emotion, communicative intent, pain, and neurological or physiological status of the source person without displaying the source person's face. Face de-identification would enable video archive sharing among researchers and clinicians without compromising privacy or confidentiality. Moreover, a version of this system could potentially be used to preserve privacy and anonymity in internet-based interviews. Innovation. The project has four innovations. The approach (1) Removes identity information while retaining facial dynamics; thus preserving the information value of the face to communicate emotion, pain, and related states. (2) Accommodates subtle and spontaneous facial actions, rather than imitating only some predefined molar expressions (e.g., happy or sad). (3) Requires no training steps by target persons. (4) And requires no hand annotation of video. The system is entirely automatic. Approach. The software will take as input a video with the face of a subject (source) and automatically generate or output a video with the face de-identified. The project will use new machine learning and computer vision algorithms for transferring subtle facial expression from a source subject (original video) to a target subject, using only one frontal image of the target subject. A major novelty of the approach is to make the process completely automatic. The algorithm will be validated using commercially available software for face recognition and custom software for facial expression analysis.