  We propose a novel 3D face recognition algorithm using a deep convolutional
neural network (DCNN) and a 3D augmentation technique. The performance of 2D
face recognition algorithms has significantly increased by leveraging the
representational power of deep neural networks and the use of large-scale
labeled training data. As opposed to 2D face recognition, training
discriminative deep features for 3D face recognition is very difficult due to
the lack of large-scale 3D face datasets. In this paper, we show that transfer
learning from a CNN trained on 2D face images can effectively work for 3D face
recognition by fine-tuning the CNN with a relatively small number of 3D facial
scans. We also propose a 3D face augmentation technique which synthesizes a
number of different facial expressions from a single 3D face scan. Our proposed
method shows excellent recognition results on Bosphorus, BU-3DFE, and 3D-TEC
datasets, without using hand-crafted features. The 3D identification using our
deep features also scales well for large databases.
