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.
