Facial Action Unit detection based on multi-task learning strategy for unlabeled facial images in the wild

Abstract

Facial Action Unit (AU) detection often relies on highly-cost accurate labeling or inaccurate pseudo labeling techniques in recent years. How to introduce large amounts of unlabeled facial images in the wild into supervised AU detection frameworks has become a challenging problem. Additionally, nearly every type of AUs has the problem of unbalanced positive and negative samples. Inspired by other multi-task learning frameworks, we first propose a multi-task learning strategy boosting AU detection in the wild through jointing facial landmark detection and AU domain separation and reconstruction. Our introduced dual domains facial landmark detection framework can solve the lack of accurate facial landmark coordinates during the AU domain separation and reconstruction training process, while the parameters of homostructural facial extraction modules from these two similar facial tasks are shared. Moreover, we propose a pixel-level feature alignment scheme to maintain the consistency of features obtained from two separation and reconstruction processes. Furthermore, a weighted asymmetric loss is proposed to change the contribution of positive and negative samples of each type of AUs to model parameters updating. Experimental results on three widely used benchmarks demonstrate our superiority to most state-of-the-art methods for AU detection.