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

通过在记录的行为观察会话中删除面部身份来保护儿童的隐私

基本信息

批准号：
10042998
负责人：
Eakta Jain
金额：
$ 41.62万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10042998
关键词：
Address Administrator Age Algorithms Attitude Authorization documentation Behavior Behavior Disorders Behavior assessment Behavioral Behavioral Research Benchmarking Child Clinical Clinical Research Code Complex Computer Vision Systems Computing Methodologies Cues Data Data Discovery Data Set Databases Development Diagnosis Diagnostic Effectiveness Eligibility Determination Equilibrium Excision Eye Face Foundations Goals Gold Health Care Costs Image Intervention Lead Literature Longevity Methodology Methods Nature Network-based Outcome Output Patients Performance Persons Privacy Privatization Process Process Assessment Remote Consultation Research Research Personnel Resource Sharing Resources Rest Services Signal Transduction Skin Source Techniques Technology Testing Text Time Training Training Technics Video Recording Videotape Voice Work autism diagnostic observation schedule autism spectrum disorder autistic children base behavior observation biomarker identification clinical decision-making clinical diagnostics clinical practice data sharing deep neural network design facial recognition algorithm facial recognition software falls gaze health care delivery heterogenous data improved insight interest multimodal data neural network novel patient privacy preservation psychologic two-dimensional usability

项目摘要

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.

项目总结/文摘