Name
Title | ||
|---|---|---|
Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video. |
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, 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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CVPRW.2016.191 | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
DocType | Volume | Issue |
Conference | abs/1605.00894 | 1 |
ISSN | Citations | PageRank |
2160-7508 | 13 | 0.61 |
References | Authors | |
26 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Zhou | 1 | 327 | 54.75 |
| Xiaopeng Hong | 2 | 379 | 42.27 |
| Fei Su | 3 | 691 | 69.87 |
| Guoying Zhao | 4 | 3767 | 166.92 |