Visual Attention Analysis and Prediction on Human Faces for Children with Autism Spectrum Disorder

Abstract

The focus of this article is to analyze and predict the visual attention of children with Autism Spectrum Disorder (ASD) when looking at human faces. Social difficulties are the hallmark features of ASD and will lead to atypical visual attention toward various stimuli more or less, especially on human faces. Learning the visual attention of children with ASD could contribute to related research in the field of medical science, psychology, and education. We first construct a Visual Attention on Faces for Autism Spectrum Disorder (VAFA) database, which consists of 300 natural scene images with human faces and corresponding eye movement data collected from 13 children with ASD. Compared with matched typically developing (TD) controls, we quantify atypical visual attention on human faces in ASD. Statistics show that some high-level factors such as face size, facial features, face pose, and facial emotions have different impacts on the visual attention of children with ASD. Combining the feature maps extracted from the state-of-the-art saliency models, we get the visual attention model on human faces for individuals with ASD. The proposed model shows the best performance among all competitors. With the help of our proposed model, researchers in related fields could design specialized education contents containing human faces for the children with ASD or produce the specific model for rapidly screening ASD using their eye movement data.