Identifying crucial facial areas with gated fusion for early ASD detection

Abstract

The application of data mining technologies in the early identification of children with Autism Spectrum Disorder (ASD) has gained prominence. Facial image analysis has emerged as a popular method for its efficiency and scalability. However, current approaches often classify facial images into ASD categories without elucidating the specific contributions of different facial areas to the outcomes. To address this gap, we propose a novel method for ASD detection using facial images while concurrently identifying significant facial areas. Our approach integrates a Pre-trained Image Encoder to extract semantic information from the original image, a Gated Fusion Module to dynamically regulate the contribution of each pixel, and a scoring layer to predict ASD scores based on the fused feature map. Experimental validation on a publicly available dataset showcases the efficacy of our method, demonstrating commendable performance in terms of precision and recall metrics.