# Automated Pain Detection from Facial Expressions using FACS: A Review

arXiv: https://arxiv.org/abs/1811.07988 · HTML: https://mlsanity.com/paper/1811.07988/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2018.

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

- [Personalized Federated Deep Learning for Pain Estimation From Face Images](https://mlsanity.com/paper/2101.04800/) (2021)
- [Unobtrusive Pain Monitoring in Older Adults with Dementia using Pairwise and Contrastive Training](https://mlsanity.com/paper/2101.03251/) (2021)
- [Intelligent Sight and Sound: A Chronic Cancer Pain Dataset](https://mlsanity.com/paper/2204.04214/) (2022)
- [Explaining with Attribute-based and Relational Near Misses: An Interpretable Approach to Distinguishing Facial Expressions of Pain and Disgust](https://mlsanity.com/paper/2308.14163/) (2023)
- [Unsupervised learning of Data-driven Facial Expression Coding System (DFECS) using keypoint tracking](https://mlsanity.com/paper/2406.05434/) (2024)
- [GraphAU-Pain: Graph-based Action Unit Representation for Pain Intensity Estimation](https://mlsanity.com/paper/2505.19802/) (2025)

## Abstract (arXiv)

Facial pain expression is an important modality for assessing pain, especially when the patient's verbal ability to communicate is impaired. The facial muscle-based action units (AUs), which are defined by the Facial Action Coding System (FACS), have been widely studied and are highly reliable as a method for detecting facial expressions (FE) including valid detection of pain. Unfortunately, FACS coding by humans is a very time-consuming task that makes its clinical use prohibitive. Significant progress on automated facial expression recognition (AFER) has led to its numerous successful applications in FACS-based affective computing problems. However, only a handful of studies have been reported on automated pain detection (APD), and its application in clinical settings is still far from a reality. In this paper, we review the progress in research that has contributed to automated pain detection, with focus on 1) the framework-level similarity between spontaneous AFER and APD problems; 2) the evolution of system design including the recent development of deep learning methods; 3) the strategies and considerations in developing a FACS-based pain detection framework from existing research; and 4) introduction of the most relevant databases that are available for AFER and APD studies. We attempt to present key considerations in extending a general AFER framework to an APD framework in clinical settings. In addition, the performance metrics are also highlighted in evaluating an AFER or an APD system.