Autism screening for children based on appearance features across multiple paradigms

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and behavior. Current ASD screening methods often focus on single paradigms or isolated features, limiting their screening accuracy. In this work, we propose a novel method for ASD screening by integrating appearance features such as gaze points, facial expressions, and head pose across multiple paradigms. The multiple paradigms including blank-overlap, person-gaze and exogenous-cueing are designed to elicit atypical behavioral patterns in ASD children. The extracted features from video data are then classified using a Long Short-Term Memory (LSTM) model. Our method achieves a classification accuracy of 0.932 and a sensitivity of 0.947 in differentiating ASD from typically developing (TD) children. The code and dataset related to this paper are available at https://github.com/theolsy/ASDTD.