Face morphing using average face for subtle expression recognition
Junya Ueda, Katsunori Okajima · 2019
Current facial expression classifiers are created based on face images with high-intensity expressions. Therefore, the recognition accuracy is low for subtle facial expressions. In this paper, we proposed a preprocessing method of face morphing to improve the ability of the classifier based on machine learning. That is, by using the average face, it removes person-specific face differences and magnifies the signal of subtle expressions. In addition, we created an artificial subtle facial expression image dataset by using average and morphed faces for both uses in our algorithm and its verification. Finally, our proposed method, as a preprocessing in a machine learning based recognizer, significantly improved the recognition accuracy of a facial expression recognition system such as CNN.