# Video-based Analysis Reveals Atypical Social Gaze in People with Autism Spectrum Disorder

arXiv: https://arxiv.org/abs/2409.00664 · HTML: https://mlsanity.com/paper/2409.00664/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 66 references that are in the map.
- First posted on arXiv 1 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Gaze360: Physically Unconstrained Gaze Estimation in the Wild](https://mlsanity.com/paper/1910.10088/) (2019)
- [Few-Shot Adaptive Gaze Estimation](https://mlsanity.com/paper/1905.01941/) (2019)
- [LAEO-Net: revisiting people Looking At Each Other in videos](https://mlsanity.com/paper/1906.05261/) (2019)
- [Classification of Abnormal Hand Movement for Aiding in Autism Detection: Machine Learning Study](https://mlsanity.com/paper/2108.07917/) (2021)

## Built on by (most cited first, 3 of 3)

- [Fine-tuning a multimodal large language model for clinician-grade autism behavioral scoring from short home videos](https://mlsanity.com/paper/2606.27484/) (2026)
- [A Proactive Multi-Agent Dialogue Framework for Assessing Social Language Disorder Traits in Autism](https://mlsanity.com/paper/2605.22993/) (2026)
- [A Deterministic Evidence Layer for Vision-Language Autism Screening from Naturalistic Home Video](https://mlsanity.com/paper/2610.09217/) (2026)

## Abstract (arXiv)

In this study, we present a quantitative and comprehensive analysis of social gaze in people with autism spectrum disorder (ASD). Diverging from traditional first-person camera perspectives based on eye-tracking technologies, this study utilizes a third-person perspective database from the Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) interview videos, encompassing ASD participants and neurotypical individuals as a reference group. Employing computational models, we extracted and processed gaze-related features from the videos of both participants and examiners. The experimental samples were divided into three groups based on the presence of social gaze abnormalities and ASD diagnosis. This study quantitatively analyzed four gaze features: gaze engagement, gaze variance, gaze density map, and gaze diversion frequency. Furthermore, we developed a classifier trained on these features to identify gaze abnormalities in ASD participants. Together, we demonstrated the effectiveness of analyzing social gaze in people with ASD in naturalistic settings, showcasing the potential of third-person video perspectives in enhancing ASD diagnosis through gaze analysis.