Impact of Action Unit Occurrence Patterns on Detection
This work addresses a specific problem in facial expression recognition for researchers, but it appears incremental as it builds on existing methods and datasets.
The paper investigates how action unit occurrence patterns affect detection performance in facial expression analysis, finding that these patterns strongly impact evaluation metrics like F1-binary, and proposes a new deep learning approach using patterns to boost accuracy.
Detecting action units is an important task in face analysis, especially in facial expression recognition. This is due, in part, to the idea that expressions can be decomposed into multiple action units. In this paper we investigate the impact of action unit occurrence patterns on detection of action units. To facilitate this investigation, we review state of the art literature, for AU detection, on 2 state-of-the-art face databases that are commonly used for this task, namely DISFA, and BP4D. Our findings, from this literature review, suggest that action unit occurrence patterns strongly impact evaluation metrics (e.g. F1-binary). Along with the literature review, we also conduct multi and single action unit detection, as well as propose a new approach to explicitly train deep neural networks using the occurrence patterns to boost the accuracy of action unit detection. These experiments validate that action unit patterns directly impact the evaluation metrics.