  The tremendous development in deep learning has led facial expression
recognition (FER) to receive much attention in the past few years. Although 3D
FER has an inherent edge over its 2D counterpart, work on 2D images has
dominated the field. The main reason for the slow development of 3D FER is the
unavailability of large training and large test datasets. Recognition
accuracies have already saturated on existing 3D emotion recognition datasets
due to their small gallery sizes. Unlike 2D photographs, 3D facial scans are
not easy to collect, causing a bottleneck in the development of deep 3D FER
networks and datasets. In this work, we propose a method for generating a large
dataset of 3D faces with labeled emotions. We also develop a deep convolutional
neural network(CNN) for 3D FER trained on 624,000 3D facial scans. The test
data comprises 208,000 3D facial scans.
