# Personalized Automatic Estimation of Self-reported Pain Intensity from Facial Expressions

arXiv: https://arxiv.org/abs/1706.07154 · HTML: https://mlsanity.com/paper/1706.07154/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2017.

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

- [Adversarial Transfer Attacks With Unknown Data and Class Overlap](https://mlsanity.com/paper/2109.11125/) (2021)
- [Automatic Estimation of Self-Reported Pain by Trajectory Analysis in the Manifold of Fixed Rank Positive Semi-Definite Matrices](https://mlsanity.com/paper/2209.01813/) (2022)
- [Beyond One-Size-Fits-All: A Survey of Personalized Affective Computing in Human-Agent Interaction](https://mlsanity.com/paper/2304.00377/) (2023)
- [Towards Synthetic Data Generation for Improved Pain Recognition in Videos under Patient Constraints](https://mlsanity.com/paper/2409.16382/) (2024)
- [Gender Fairness of Machine Learning Algorithms for Pain Detection](https://mlsanity.com/paper/2506.11132/) (2025)
- [Emotion recognition techniques with rule based and machine learning approaches](https://mlsanity.com/paper/2103.00658/) (2021)
- [Towards Personalized Intelligence at Scale](https://mlsanity.com/paper/2203.06668/) (2022)
- [Pain Analysis using Adaptive Hierarchical Spatiotemporal Dynamic Imaging](https://mlsanity.com/paper/2312.06920/) (2023)
- [PainNet: Statistical Relation Network with Episode-Based Training for Pain Estimation](https://mlsanity.com/paper/2504.06257/) (2025)

## Abstract (arXiv)

Pain is a personal, subjective experience that is commonly evaluated through visual analog scales (VAS). While this is often convenient and useful, automatic pain detection systems can reduce pain score acquisition efforts in large-scale studies by estimating it directly from the participants' facial expressions. In this paper, we propose a novel two-stage learning approach for VAS estimation: first, our algorithm employs Recurrent Neural Networks (RNNs) to automatically estimate Prkachin and Solomon Pain Intensity (PSPI) levels from face images. The estimated scores are then fed into the personalized Hidden Conditional Random Fields (HCRFs), used to estimate the VAS, provided by each person. Personalization of the model is performed using a newly introduced facial expressiveness score, unique for each person. To the best of our knowledge, this is the first approach to automatically estimate VAS from face images. We show the benefits of the proposed personalized over traditional non-personalized approach on a benchmark dataset for pain analysis from face images.