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Recognizing facial expressions is one of the central problems in computervision. Temporal image sequences have useful spatio-temporal features forrecognizing expressions. In this paper, we propose a new 3D Convolution NeuralNetwork (CNN) that can be trained end-to-end for facial expression recognitionon temporal image sequences without using facial landmarks. More specifically,a novel 3D convolutional layer that we call Local Binary Volume (LBV) layer isproposed. The LBV layer, when used with our newly proposed LBVCNN network,achieve comparable results compared to state-of-the-art landmark-based orwithout landmark-based models on image sequences from CK+, Oulu-CASIA, and UNBCMcMaster shoulder pain datasets. Furthermore, our LBV layer reduces the numberof trainable parameters by a significant amount when compared to a conventional3D convolutional layer. As a matter of fact, when compared to a 3x3x3conventional 3D convolutional layer, the LBV layer uses 27 times less trainableparameters.

LBVCNN: Local Binary Volume Convolutional Neural Network for Facial Expression Recognition from Image Sequences