We propose a novel 3D face recognition algorithm using a deep convolutional neural network (DCNN) and a 3D face expression 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. 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 an extremely small number of 3D facial scans. We also propose a 3D face expression 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 face identification using our deep features also scales well for large databases.