Faster R-CNN-MobileNetV3 Based Micro Expression Detection for Autism Spectrum Disorder

Hanni Li; Yutong Gu; Jiarui Han; Yimeng Sun; Hongwei Lei; Chen Li; Ning Xu

doi:10.53941/aim.2025.100002

Abstract

Autism spectrum disorder (ASD) is a neuropathic disease which is characterized by deficits in social interaction and communication. Therefore, the ASD patients have weak ability to express themselves or let others know about their thoughts. As society pays more attention to ASD patients, early intervention programs, behavioral therapy and technological assistance have emerged to help ASD patients improve their quality of lives. This paper aims to propose an improved object detection algorithm based on Faster R-CNN-MobileNetV3 to analyze the micro expressions of ASD patients. The data set includes 1358 face images of ASD patients built from 12 ASD movies with the method of Cinemetrics. Through the training and testing of the ASD data set with the improved model, the overall precision rate has reached 0.9 and mean Average Precision also has significant improvement. As a result, the improved Faster R-CNN-MobileNetV3 model achieves a good performance to recognize micro expressions and emotions of ASD patients.

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