Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video
This work addresses automatic pain intensity estimation for healthcare applications, but it is incremental as it builds on existing methods with a hybrid approach.
The paper tackles the problem of unstable pain intensity estimation from video frames by proposing a recurrent convolutional neural network regression framework that considers historical frames, achieving promising accuracy and real-time speed on the UNBC-McMaster database.
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.