# Face Alignment In-the-Wild: A Survey

arXiv: https://arxiv.org/abs/1608.04188 · HTML: https://mlsanity.com/paper/1608.04188/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2016.

## Built on by (most cited first, 6 of 6)

- [InfAnFace: Bridging the infant-adult domain gap in facial landmark estimation in the wild](https://mlsanity.com/paper/2110.08935/) (2021)
- [MetaHead: An Engine to Create Realistic Digital Head](https://mlsanity.com/paper/2304.00838/) (2023)
- [A survey and classification of face alignment methods based on face models](https://mlsanity.com/paper/2311.03082/) (2023)
- [Distilling Facial Knowledge With Teacher-Tasks: Semantic-Segmentation-Features For Pose-Invariant Face-Recognition](https://mlsanity.com/paper/2209.01115/) (2022)
- [xTrace: A Facial Expressive Behaviour Analysis Tool for Continuous Affect Recognition](https://mlsanity.com/paper/2505.05043/) (2025)
- [CanonicalPhys: Pose-Robust Remote Photoplethysmography via Canonical-Space Priors](https://mlsanity.com/paper/2607.15995/) (2026)

## Abstract (arXiv)

Over the last two decades, face alignment or localizing fiducial facial points has received increasing attention owing to its comprehensive applications in automatic face analysis. However, such a task has proven extremely challenging in unconstrained environments due to many confounding factors, such as pose, occlusions, expression and illumination. While numerous techniques have been developed to address these challenges, this problem is still far away from being solved. In this survey, we present an up-to-date critical review of the existing literatures on face alignment, focusing on those methods addressing overall difficulties and challenges of this topic under uncontrolled conditions. Specifically, we categorize existing face alignment techniques, present detailed descriptions of the prominent algorithms within each category, and discuss their advantages and disadvantages. Furthermore, we organize special discussions on the practical aspects of face alignment in-the-wild, towards the development of a robust face alignment system. In addition, we show performance statistics of the state of the art, and conclude this paper with several promising directions for future research.