# Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video

arXiv: https://arxiv.org/abs/1605.00894 · HTML: https://mlsanity.com/paper/1605.00894/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2016.

## Built on by (most cited first, 12 of 12)

- [Unobtrusive Pain Monitoring in Older Adults with Dementia using Pairwise and Contrastive Training](https://mlsanity.com/paper/2101.03251/) (2021)
- [Facial Expression Analysis Using Decomposed Multiscale Spatiotemporal Networks](https://mlsanity.com/paper/2203.11111/) (2022)
- [Temporal Deep Learning Architecture for Prediction of COVID-19 Cases in India](https://mlsanity.com/paper/2108.13823/) (2021)
- [Intelligent Sight and Sound: A Chronic Cancer Pain Dataset](https://mlsanity.com/paper/2204.04214/) (2022)
- [Fully-attentive and interpretable: vision and video vision transformers for pain detection](https://mlsanity.com/paper/2210.15769/) (2022)
- [Can AI detect pain and express pain empathy? A review from emotion recognition and a human-centered AI perspective](https://mlsanity.com/paper/2110.04249/) (2021)
- [Chronic pain patient narratives allow for the estimation of current pain intensity](https://mlsanity.com/paper/2210.17473/) (2022)
- [Chronic Pain and Language: A Topic Modelling Approach to Personal Pain Descriptions](https://mlsanity.com/paper/2109.00402/) (2021)
- [Pain Analysis using Adaptive Hierarchical Spatiotemporal Dynamic Imaging](https://mlsanity.com/paper/2312.06920/) (2023)
- [Improving Pain Classification using Spatio-Temporal Deep Learning Approaches with Facial Expressions](https://mlsanity.com/paper/2501.06787/) (2025)
- [GraphAU-Pain: Graph-based Action Unit Representation for Pain Intensity Estimation](https://mlsanity.com/paper/2505.19802/) (2025)
- [ReMiX-MAE: Learning Missing-Channel Cross-Modal Representations from RGB-Only Clinical Facial Videos for Sympathetic-Mediated Pain Assessment](https://mlsanity.com/paper/2608.02561/) (2026)

## Abstract (arXiv)

Automatic pain intensity estimation possesses a significant position in healthcare and medical field. Traditional static methods prefer to extract features from frames separately in a video, which would result in unstable changes and peaks among adjacent frames. To overcome this problem, we propose a real-time regression framework based on the recurrent convolutional neural network for automatic frame-level pain intensity estimation. Given vector sequences of AAM-warped facial images, we used a sliding-window strategy to obtain fixed-length input samples for the recurrent network. We then carefully design the architecture of the recurrent network to output continuous-valued pain intensity. The proposed end-to-end pain intensity regression framework can predict the pain intensity of each frame by considering a sufficiently large historical frames while limiting the scale of the parameters within the model. Our method achieves promising results regarding both accuracy and running speed on the published UNBC-McMaster Shoulder Pain Expression Archive Database.