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.
@article{arxiv.1605.00894,
title = {Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video},
author = {Jing Zhou and Xiaopeng Hong and Fei Su and Guoying Zhao},
journal= {arXiv preprint arXiv:1605.00894},
year = {2016}
}
Comments
This paper is the pre-print technical report of the paper accepted by the IEEE CVPR Workshop of Affect "in-the-wild". The final version will be available after the workshop