Face Emotion Recognization Using Dataset Augmentation Based on Neural Network

Face expression plays a critical role during the daily life, and people cannot live without face emotion. With the development of technology, many methods of facial expression recognition have been proposed. However, from traditional methods to deep learning methods, few of them pay attention to the hybrid data augmentation, which can help improve the robustness of models. Therefore, a method of hybrid data augmentation is highlighted in this paper. The hybrid data augmentation is a method of combining several effective data augmentation. In the experiments, the technique is applied on four basic networks and the results are compared to the baseline models. After applying this technique, the results show that four benchmark models have higher performance than those previously. This approach is simple and robust in terms of data augmentation, which makes it applicated in the real world in the future. Besides the results show versatility of the technique as all of our experiments get better results.