Abstract

Visual behavior, and specifically gaze directed at objects and people, is a fundamental cue for understanding how children with Autism Spectrum Disorder (ASD) experience and respond to social interaction. Indeed atypical behaviors such as averting the gaze from faces, looking out of the corner of the eyes, and having difficulties disengaging from non-social stimuli, are well known symptoms of this developmental disorder. However, studying these atypicalities presents several technical challenges, both at a hardware and software level. Traditionally this type of analysis is done by viewing video recordings of the child’s interaction and manually rating gaze behavior episodes. This data collection procedure is often a time consuming process, and all the results have to be checked by multiple raters to ensure reliability. When automated systems are used, issues of intrusiveness, robustness and reliability have to be taken into consideration, and their impact on the behavior of the child needs to be accounted for. Moreover, children often do not cooperate to the same extent as