# InfAnFace: Bridging the infant-adult domain gap in facial landmark estimation in the wild

arXiv: https://arxiv.org/abs/2110.08935 · HTML: https://mlsanity.com/paper/2110.08935/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 17 Oct 2021.

## Builds on (most cited first, 15 of 15)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [RetinaFace: Single-stage Dense Face Localisation in the Wild](https://mlsanity.com/paper/1905.00641/) (2019)
- [Face Alignment in Full Pose Range: A 3D Total Solution](https://mlsanity.com/paper/1804.01005/) (2018)
- [Facial Landmark Detection: a Literature Survey](https://mlsanity.com/paper/1805.05563/) (2018)
- [3FabRec: Fast Few-shot Face alignment by Reconstruction](https://mlsanity.com/paper/1911.10448/) (2019)
- [Invariant Representation Learning for Infant Pose Estimation with Small Data](https://mlsanity.com/paper/2010.06100/) (2020)
- [Adversarial Weighting for Domain Adaptation in Regression](https://mlsanity.com/paper/2006.08251/) (2020)
- [In-Bed Pose Estimation: Deep Learning with Shallow Dataset](https://mlsanity.com/paper/1711.01005/) (2017)
- [Going Deeper Into Face Detection: A Survey](https://mlsanity.com/paper/2103.14983/) (2021)
- [Face Alignment In-the-Wild: A Survey](https://mlsanity.com/paper/1608.04188/) (2016)
- [Facial Expression and Peripheral Physiology Fusion to Decode Individualized Affective Experience](https://mlsanity.com/paper/1811.07392/) (2018)

## Built on by (most cited first, 9 of 9)

- [Neonatal Face and Facial Landmark Detection from Video Recordings](https://mlsanity.com/paper/2302.04341/) (2023)
- [A Video-based End-to-end Pipeline for Non-nutritive Sucking Action Recognition and Segmentation in Young Infants](https://mlsanity.com/paper/2303.16867/) (2023)
- [Automatic Assessment of Infant Face and Upper-Body Symmetry as Early Signs of Torticollis](https://mlsanity.com/paper/2210.15022/) (2022)
- [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)
- [Overcoming Small Data Limitations in Video-Based Infant Respiration Estimation](https://mlsanity.com/paper/2512.06888/) (2025)
- [Subtle Signals: Video-based Detection of Infant Non-nutritive Sucking as a Neurodevelopmental Cue](https://mlsanity.com/paper/2310.16138/) (2023)
- [Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the Art](https://mlsanity.com/paper/2311.12300/) (2023)
- [NeoLoc-68: End-to-end 68-point neonatal facial landmark localisation in neonatal clinical environments](https://mlsanity.com/paper/2606.20823/) (2026)

## Abstract (arXiv)

We lay the groundwork for research in the algorithmic comprehension of infant faces, in anticipation of applications from healthcare to psychology, especially in the early prediction of developmental disorders. Specifically, we introduce the first-ever dataset of infant faces annotated with facial landmark coordinates and pose attributes, demonstrate the inadequacies of existing facial landmark estimation algorithms in the infant domain, and train new state-of-the-art models that significantly improve upon those algorithms using domain adaptation techniques. We touch on the closely related task of facial detection for infants, and also on a challenging case study of infrared baby monitor images gathered by our lab as part of in-field research into the aforementioned developmental issues.