Using Synthetic Data to Improve Facial Expression Analysis with 3D Convolutional Networks

Over the past few years, neural networks have made a huge improvement in object recognition and event analysis. However, due to a lack of available data, neural networks were not efficiently applied in expression analysis. In this paper, we tackle the problem of facial expression analysis using deep neural network by generating a realistic large scale synthetic labeled dataset. We train a deep 3-dimensional convolutional network on the generated dataset and empirically show how the presented method can efficiently classify facial expressions. Our method addresses four fundamental issues: (i) generating a large scale facial expression dataset that is realistic and accurate, (ii) a rich spatial representation of expressions, (iii) better spatiotemporal feature learning compared to recent techniques and (iv) with a simple linear classifier our learned features outperform state-of-the-art methods.