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

## TL;DR

This paper introduces a novel two-stage deep learning approach that personalizes automatic pain intensity estimation from facial expressions, significantly improving accuracy over non-personalized methods.

## Contribution

It presents the first method to automatically estimate visual analog scale (VAS) pain scores from face images using personalized models with a new facial expressiveness score.

## Key findings

- Personalized approach outperforms non-personalized methods on benchmark datasets.
- Recurrent Neural Networks effectively estimate pain levels from facial expressions.
- Personalization improves the accuracy of automatic pain assessment.

## Abstract

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.