# A Facial Affect Analysis System for Autism Spectrum Disorder

arXiv: https://arxiv.org/abs/1904.03616 · HTML: https://mlsanity.com/paper/1904.03616/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2019.

## Built on by (most cited first, 5 of 5)

- [Classification of Abnormal Hand Movement for Aiding in Autism Detection: Machine Learning Study](https://mlsanity.com/paper/2108.07917/) (2021)
- [Exploring Spatial-Temporal Dynamics in Event-based Facial Micro-Expression Analysis](https://mlsanity.com/paper/2508.11988/) (2025)
- [Localizing Moments of Actions in Untrimmed Videos of Infants with Autism Spectrum Disorder](https://mlsanity.com/paper/2404.05849/) (2024)
- [Hybrid Quantum Deep Learning Model for Emotion Detection using raw EEG Signal Analysis](https://mlsanity.com/paper/2411.17715/) (2024)
- [More comprehensive facial inversion for more effective expression recognition](https://mlsanity.com/paper/2211.13564/) (2022)

## Abstract (arXiv)

In this paper, we introduce an end-to-end machine learning-based system for classifying autism spectrum disorder (ASD) using facial attributes such as expressions, action units, arousal, and valence. Our system classifies ASD using representations of different facial attributes from convolutional neural networks, which are trained on images in the wild. Our experimental results show that different facial attributes used in our system are statistically significant and improve sensitivity, specificity, and F1 score of ASD classification by a large margin. In particular, the addition of different facial attributes improves the performance of ASD classification by about 7% which achieves a F1 score of 76%.