  Automatic pain intensity assessment has a high value in disease diagnosis
applications. Inspired by the fact that many diseases and brain disorders can
interrupt normal facial expression formation, we aim to develop a computational
model for automatic pain intensity assessment from spontaneous and micro facial
variations. For this purpose, we propose a 3D deep architecture for dynamic
facial video representation. The proposed model is built by stacking several
convolutional modules where each module encompasses a 3D convolution kernel
with a fixed temporal depth, several parallel 3D convolutional kernels with
different temporal depths, and an average pooling layer. Deploying variable
temporal depths in the proposed architecture allows the model to effectively
capture a wide range of spatiotemporal variations on the faces. Extensive
experiments on the UNBC-McMaster Shoulder Pain Expression Archive database show
that our proposed model yields in a promising performance compared to the
state-of-the-art in automatic pain intensity estimation.
