On pain assessment from facial videos using spatio-temporal local descriptors

Automatically recognizing pain from spontaneous facial expression is of increased attention, since it can provide for a direct and relatively objective indication to pain experience. Until now, most of the existing works have focused on analyzing pain from individual images or video-frames, hence discarding the spatio-temporal information that can be useful in the continuous assessment of pain. In this context, this paper investigates and quantifies for the first time the role of the spatio-temporal information in pain assessment by comparing the performance of several baseline local descriptors used in their traditional spatial form against their spatio-temporal counterparts that take into account the video dynamics. For this purpose, we perform extensive experiments on two benchmark datasets. Our results indicate that using spatio-temporal information to classify video-sequences consistently shows superior performance when compared against the one obtained using only static information.