Mimicking the Annotation Process for Recognizing the Micro Expressions

Micro-expression recognition (MER) has recently become a popular research topic due to its wide applications, e.g., movie rating and recognizing the neurological disorder. By virtue of deep learning techniques, the performance of MER has been significantly improved and reached unprecedented results. This paper proposes a novel architecture to mimic how the expressions are annotated. Specifically, during the annotation process in several datasets, the AU labels are first obtained with FACS, and the expression labels are then decided based on the combinations of the AU labels. Meanwhile, these AU labels describe either the eyes or mouth movements (mutually-exclusive). Following this idea, we design a dual-branch structure with a new augmentation method to separately capture the eyes and mouth features and teach the model what the general expressions should be. Moreover, to adaptively fuse the area features for different expressions, we propose Area Weighted Module to assign different weights to each region. Additionally, we set up an auxiliary task to align the AU similarity scores to help our model capture facial patterns further with AU labels. The proposed approach outperforms other state-of-the-art methods in terms of accuracy on the CASME II and SAMM datasets. Moreover, we provide a new visualization approach to show the relationship between the facial regions and AU features.