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Automatic pain intensity assessment has a high value in disease diagnosisapplications. Inspired by the fact that many diseases and brain disorders caninterrupt normal facial expression formation, we aim to develop a computationalmodel for automatic pain intensity assessment from spontaneous and micro facialvariations. For this purpose, we propose a 3D deep architecture for dynamicfacial video representation. The proposed model is built by stacking severalconvolutional modules where each module encompasses a 3D convolution kernelwith a fixed temporal depth, several parallel 3D convolutional kernels withdifferent temporal depths, and an average pooling layer. Deploying variabletemporal depths in the proposed architecture allows the model to effectivelycapture a wide range of spatiotemporal variations on the faces. Extensiveexperiments on the UNBC-McMaster Shoulder Pain Expression Archive database showthat our proposed model yields in a promising performance compared to thestate-of-the-art in automatic pain intensity estimation.

Deep Spatiotemporal Representation of the Face for Automatic Pain Intensity Estimation