Early detection of children with Autism Spectrum Disorder based on visual exploration of images

Autism Spectrum Disorder is a developmental disorder characterized by a deficit in social behaviour and specific interactions such as reduced eye contact and body gestures. Recent advancements in software and hardware multimedia technologies provide the tools for early detecting the presence of this disorder. In this paper we present an approach based on the combined use of machine learning and eye tracking information. More specifically, features are extracted from image content and viewing behaviour, such as the presence of objects and fixations towards the centre of a scene. Those features are used to train a machine learning-based classifier. The obtained results show that the considered features allow to identify children affected by autism spectrum disorder and typically developing ones.