Improving Performance of Facial Expression Recognition using Multi-task Learning of Neural Networks

Facial expression recognition is an important topic in the field of human-agent interaction, because facial expression is simple and impressive signal which human can send to others. Though there have been numerous studies on facial image analysis, the performance of expression recognition is still not acceptable due to the diversity of human expression and enormous variations in facial images. In this paper, we try to improve the performance of facial expression recognition by using multi-task learning techniques of neural networks. Through computational experiments on a benchmark database, we show positive possibility of performance improvement using multi-task learning.