# Automatic Assessment of Infant Face and Upper-Body Symmetry as Early Signs of Torticollis

arXiv: https://arxiv.org/abs/2210.15022 · HTML: https://mlsanity.com/paper/2210.15022/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Distribution-Aware Coordinate Representation for Human Pose Estimation](https://mlsanity.com/paper/1910.06278/) (2019)
- [Invariant Representation Learning for Infant Pose Estimation with Small Data](https://mlsanity.com/paper/2010.06100/) (2020)
- [Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences](https://mlsanity.com/paper/1810.07538/) (2018)
- [InfAnFace: Bridging the infant-adult domain gap in facial landmark estimation in the wild](https://mlsanity.com/paper/2110.08935/) (2021)
- [Computer Vision to the Rescue: Infant Postural Symmetry Estimation from Incongruent Annotations](https://mlsanity.com/paper/2207.09352/) (2022)

## Built on by (most cited first, 2 of 2)

- [Leveraging Synthetic Adult Datasets for Unsupervised Infant Pose Estimation](https://mlsanity.com/paper/2504.05789/) (2025)
- [Multiple Toddler Tracking in Indoor Videos](https://mlsanity.com/paper/2311.17656/) (2023)

## Abstract (arXiv)

We apply computer vision pose estimation techniques developed expressly for the data-scarce infant domain to the study of torticollis, a common condition in infants for which early identification and treatment is critical. Specifically, we use a combination of facial landmark and body joint estimation techniques designed for infants to estimate a range of geometric measures pertaining to face and upper body symmetry, drawn from an array of sources in the physical therapy and ophthalmology research literature in torticollis. We gauge performance with a range of metrics and show that the estimates of most these geometric measures are successful, yielding strong to very strong Spearman's ρ correlation with ground truth values. Furthermore, we show that these estimates, derived from pose estimation neural networks designed for the infant domain, cleanly outperform estimates derived from more widely known networks designed for the adult domain