Towards Facial De-Expression and Expression Recognition in the Wild
Jiahui Hu, Bing Yu, Yun Yang, Bailan Feng · 2019
Previous work has shown that a facial expression is able to be decomposed into an expressive component and an neutral component. Many researchers have utilized this principle to implement the facial expression classification with paired laboratory-controlled datasets where each expression image has a corresponding neutral face image of the same identity. However, existing approaches cannot deal with unpaired in-the-wild datasets where most of the expression images lack their corresponding neutral counterparts. In this paper, we propose a new de-expression method based on domain transfer techniques that applies the above principle to unpaired in-the-wild datasets. Different expressions are treated as different image domains so that domain transfer techniques (such as generative adversarial networks) are able to be adopted to generate neutral face images for in-the-wild datasets. In such way, we obtain better expression recognition accuracy and alleviate the problem of lacking paired expression images. Experimental results conducted on three well-known public datasets, CK+, RAF and FER2013, validate the effectiveness of our proposed method. The results shows that our proposed approach outperforms the state-of-the-art methods on both laboratory-controlled and in-the-wild datasets.