Protecting the privacy of the child through facial identity removal in recorded behavioral observation sessions

项目总结/摘要 患有自闭症谱系障碍（ASD）的儿童在其一生中受益于专门服务。 这些服务的资格是通过行为评估和临床观察确定的。为了努力 使评估过程更快，更可靠，更容易获得，几十年来， 研究识别标志物，提供深入了解ASD。例如，凝视跟随、眼睛跟随 接触和凝视厌恶都是与自闭症评估相关的线索。通过行为观察 评估，特别是在临床研究环境中，通常被录像，用于重复 观察、记录保存、培训和评估干预的纵向影响。然而，这些视频 通常也是探索细微差别和行为信号的基础，这可能会导致研究人员 甚至更强和更可靠的标记信号，以利于临床决策过程。更 最近，诸如计算机视觉技术的计算方法的进步已经开始提供新的 发现和鉴定与ASD相关的新型行为标记的途径。调谐和 这些技术的训练依赖于用于发现的注释视频数据的可用性。然而，尽管 评估视频数据集对于这些研究目的具有独特的价值，很难共享它们 出于隐私考虑。因此，目前的做法是严格控制对视频的访问 sessions.本研究提出了视频观察会话的隐私机制的发展， 特别关注保护被观察儿童面部特征的机制， 保留对诊断评估至关重要的基于凝视的信息。我们的方法是实现已知的 第三人称视频监控文献中的隐私机制以及基于 为非真实感渲染和基于深度神经网络的面部转移开发的算法， 适用于这个领域。我们将根据提供的匿名化程度和 对行为评估的影响。这项研究的结果将是一个表征已知的 以及关于匿名化和行为观察的效用的新颖的隐私机制。是我们 这项研究的目标是创造技术，以促进隐私启用视频数据共享，从而加快 ASD的计算和行为研究。私人视频也可用于创建培训材料 为行为分析师，从而增加了内容的广度，他们可以审查在他们的培训，允许 更快的培训，实现远程/在线培训，并为临床远程咨询创造机会 实践

PROJECT SUMMARY/ABSTRACT Children with autism spectrum disorders (ASD) benefit from specialized services throughout their lifespan. Eligibility for these services is determined by behavioral assessments and clinical observation. In an effort to make the assessment process faster, more reliable, and more accessible, there have been decades of research on the identification of markers that provide insight into ASD. For example, gaze following, eye contact, and gaze aversion are all cues relevant to the assessment of autism. Observations through behavioral assessments, especially in clinical research settings, are often videotaped for purposes such as repeat viewing, record keeping, training, and assessing the longitudinal impact of intervention. However, these videos are often also the basis for explorations of nuances and behavioral signals that may lead to researchers to even stronger and more reliable marker signals to the benefit of the clinical decision-making process. More recently, advances in computational methods such as computer vision techniques have begun to provide new avenues into the discovery and identification of novel behavioral markers associated with ASD. The tuning and training of these techniques relies on the availability of annotated video data for discovery. However, though assessment video datasets are uniquely valuable for these research purposes, it is difficult to share them because of privacy considerations. As a result, the current practice is to tightly control access to video sessions. This study proposes the development of privacy mechanisms for video observation sessions, specifically focusing on mechanisms that protect the facial identity of the child under observation while retaining gaze-based information critical for diagnostic assessments. Our approach is to implement known privacy mechanisms from the third-person video surveillance literature as well as novel mechanisms based on algorithms developed for non-photorealistic rendering and deep neural network based facial transfer that are applicable to this domain. We will evaluate these mechanisms on the extent of anonymization provided and the impact they have on behavioral assessment. The output of this research will be a characterization of known and novel privacy mechanisms with respect to utility for anonymization and behavioral observation. It is our goal that this study create technology to facilitate privacy enabled video data sharing, thus accelerating computational and behavioral research for ASD. Private videos can also be used to create training materials for behavioral analysts, thus increasing the breadth of content they can review in their training, allowing for faster training, enabling remote/online training, and creating opportunities for remote consultation in clinical practice.