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 slidingwindow strategy to obtain fixed-length input samples for the recurrent network. We then carefully design the architecture of the recurrent network to output continuousvalued 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 UNBCMcMaster Shoulder Pain Expression Archive Database.