Analysis of Training Data to Improve the Accuracy of Facial Expression Recognition

Abstract

A study on communication robots has become popular in recent years, and the needs of an intelligent human-machine interface are increased. Facial expression leads itself to a better understanding of human emotions because it plays a crucial part in inter-human communication. Facial expression contains various patterns, and unlearned new patterns appear over time. Therefore, a recognition model needs to adapt to new patterns and evolve. In previous studies, a method has been proposed that enables additional learning of new facial expression patterns using a threshold value called a vigilance parameter. When additional learning was performed, the average accuracy was less than 90%, and it was found that sufficient average accuracy could not be obtained. In this study, we analyzed the tendency of the training data to contribute to the improvement of recognition accuracy.