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

Jing Zhou, Xiaopeng Hong, Fei Su, Guoying Zhao; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 84-92

Abstract


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, resulting 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. 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 in both accuracy and running speed on the published UNBC-McMaster database.

Related Material


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[bibtex]
@InProceedings{Zhou_2016_CVPR_Workshops,
author = {Zhou, Jing and Hong, Xiaopeng and Su, Fei and Zhao, Guoying},
title = {Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2016}
}