Context-Aware Feature and Label Fusion for Facial Action Unit Intensity Estimation With Partially Labeled Data

Abstract

Facial action unit (AU) intensity estimation is a fundamental task for facial behaviour analysis. Most previous methods use a whole face image as input for intensity prediction. Considering that AUs are defined according to their corresponding local appearance, a few patch-based methods utilize image features of local patches. However, fusion of local features is always performed via straightforward feature concatenation or summation. Besides, these methods require fully annotated databases for model learning, which is expensive to acquire. In this paper, we propose a novel weakly supervised patch-based deep model on basis of two types of attention mechanisms for joint intensity estimation of multiple AUs. The model consists of a feature fusion module and a label fusion module. And we augment attention mechanisms of these two modules with a learnable task-related context, as one patch may play different roles in analyzing different AUs and each AU has its own temporal evolution rule. The context-aware feature fusion module is used to capture spatial relationships among local patches while the context-aware label fusion module is used to capture the temporal dynamics of AUs. The latter enables the model to be trained on a partially annotated database. Experimental evaluations on two benchmark expression databases demonstrate the superior performance of the proposed method.