Hugging Rain Man: A Novel Facial Action Units Dataset for Analyzing Atypical Facial Expressions in Children with Autism Spectrum Disorder
Yanfeng Ji, Shutong Wang, Ruyi Xu, Jingying Chen, Xinzhou Jiang,, Zhengyu Deng, Yuxuan Quan, Junpeng Liu

TL;DR
This paper introduces the Hugging Rain Man dataset with annotated facial action units for children with ASD and typical development, revealing significant differences in facial expressions and providing tools for early ASD screening.
Contribution
The paper presents a novel, large-scale dataset with detailed annotations and a new analysis method for understanding and detecting atypical facial expressions in children with ASD.
Findings
Significant differences in facial action units between ASD and TD groups.
A new temporal regression method for analyzing dynamic facial sequences.
Baseline AU detection results for future research.
Abstract
Children with Autism Spectrum Disorder (ASD) often exhibit atypical facial expressions. However, the specific objective facial features that underlie this subjective perception remain unclear. In this paper, we introduce a novel dataset, Hugging Rain Man (HRM), which includes facial action units (AUs) manually annotated by FACS experts for both children with ASD and typical development (TD). The dataset comprises a rich collection of posed and spontaneous facial expressions, totaling approximately 130,000 frames, along with 22 AUs, 10 Action Descriptors (ADs), and atypicality ratings. A statistical analysis of static images from the HRM reveals significant differences between the ASD and TD groups across multiple AUs and ADs when displaying the same emotional expressions, confirming that participants with ASD tend to demonstrate more irregular and diverse expression patterns.…
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Taxonomy
TopicsAutism Spectrum Disorder Research
