An Efficient Algorithm of Facial Expression Recognition by TSG-RNN Network

Facial expression recognition remains a challenging problem and the small datasets further exacerbate the task. Most previous works realize facial expression by fine-tuning the network pre-trained on a related domain. They have limitations inevitably. In this paper, we propose an optimal CNN model by transfer learning and fusing three characteristics: spatial, temporal and geometric information. Also, the proposed CNN module is composed of two-fold structures and it can implement a fast training. Evaluation experiments show that the proposed method is comparable to or better than most of the state-of-the-art approaches in both recognition accuracy and training speed.