  Recognizing facial expressions is one of the central problems in computer
vision. Temporal image sequences have useful spatio-temporal features for
recognizing expressions. In this paper, we propose a new 3D Convolution Neural
Network (CNN) that can be trained end-to-end for facial expression recognition
on temporal image sequences without using facial landmarks. More specifically,
a novel 3D convolutional layer that we call Local Binary Volume (LBV) layer is
proposed. The LBV layer, when used with our newly proposed LBVCNN network,
achieve comparable results compared to state-of-the-art landmark-based or
without landmark-based models on image sequences from CK+, Oulu-CASIA, and UNBC
McMaster shoulder pain datasets. Furthermore, our LBV layer reduces the number
of trainable parameters by a significant amount when compared to a conventional
3D convolutional layer. As a matter of fact, when compared to a 3x3x3
conventional 3D convolutional layer, the LBV layer uses 27 times less trainable
parameters.
