Risk assessment and automatic identification of autistic children based on appearance

The diagnosis of Autism Spectrum Disorder (ASD) is mainly based on some diagnostic scales and evaluations by professional doctors, which may have limitations such as subjectivity, time, and cost. This research introduces a novel assessment and auto-identification approach for autistic children based on the appearance of children, which is a relatively objective, fast, and cost-effective approach. Initially, a custom social interaction scenario was developed, followed by a facial data set (ACFD) that contained 187 children, including 92 ASD and 95 children typically developing (TD). Using computer vision techniques, some appearance features of children including facial appearing time, eye concentration analysis, response time to name calls, and emotional expression ability were extracted. Subsequently, these features were combined and machine learning methods were used for the classification of children. Notably, the Bayes classifier achieved a remarkable accuracy of 94.1%. The experimental results show that the extracted visual appearance features can reflect the typical symptoms of children, and the automatic recognition method can provide an auxiliary diagnosis or data support for doctors.