# Facial Feature Point Detection: A Comprehensive Survey

arXiv: https://arxiv.org/abs/1410.1037 · HTML: https://mlsanity.com/paper/1410.1037/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2014.

## Built on by (most cited first, 13 of 13)

- [Fast Facial Landmark Detection and Applications: A Survey](https://mlsanity.com/paper/2101.10808/) (2021)
- [Graph-based Facial Affect Analysis: A Review](https://mlsanity.com/paper/2103.15599/) (2021)
- [Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion](https://mlsanity.com/paper/2307.13463/) (2023)
- [Oriented object detection in optical remote sensing images using deep learning: a survey](https://mlsanity.com/paper/2302.10473/) (2023)
- [Deep Learning Approaches for Seizure Video Analysis: A Review](https://mlsanity.com/paper/2312.10930/) (2023)
- [Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States](https://mlsanity.com/paper/2206.08405/) (2022)
- [Interpretable Multimodal Emotion Recognition using Facial Features and Physiological Signals](https://mlsanity.com/paper/2306.02845/) (2023)
- [Infinite 3D Landmarks: Improving Continuous 2D Facial Landmark Detection](https://mlsanity.com/paper/2405.20117/) (2024)
- [FFR_FD: Effective and Fast Detection of DeepFakes Based on Feature Point Defects](https://mlsanity.com/paper/2107.02016/) (2021)
- [Skin feature point tracking using deep feature encodings](https://mlsanity.com/paper/2112.14159/) (2021)
- [Automatic 3D Registration of Dental CBCT and Face Scan Data using 2D Projection Images](https://mlsanity.com/paper/2305.10132/) (2023)
- [Quantum-Assisted Support Vector Regression](https://mlsanity.com/paper/2111.09304/) (2021)
- [Fast Eye Detector Using Siamese Network for NIR Partial Face Images](https://mlsanity.com/paper/2202.10671/) (2022)

## Abstract (arXiv)

This paper presents a comprehensive survey of facial feature point detection with the assistance of abundant manually labeled images. Facial feature point detection favors many applications such as face recognition, animation, tracking, hallucination, expression analysis and 3D face modeling. Existing methods can be categorized into the following four groups: constrained local model (CLM)-based, active appearance model (AAM)-based, regression-based, and other methods. CLM-based methods consist of a shape model and a number of local experts, each of which is utilized to detect a facial feature point. AAM-based methods fit a shape model to an image by minimizing texture synthesis errors. Regression-based methods directly learn a mapping function from facial image appearance to facial feature points. Besides the above three major categories of methods, there are also minor categories of methods which we classify into other methods: graphical model-based methods, joint face alignment methods, independent facial feature point detectors, and deep learning-based methods. Though significant progress has been made, facial feature point detection is limited in its success by wild and real-world conditions: variations across poses, expressions, illuminations, and occlusions. A comparative illustration and analysis of representative methods provide us a holistic understanding and deep insight into facial feature point detection, which also motivates us to explore promising future directions.