Expression-assisted facial action unit recognition under incomplete AU annotation

Abstract

Facial action unit (AU) recognition is an important task for facial expression analysis. Traditional AU recognition methods typically include a supervised training, where the AU annotated training images are needed. AU annotation is a time consuming, expensive, and error prone process. While AU is hard to annotate, facial expression is relatively easy to label. To take advantage of this, we introduce a new learning method that trains an AU classifier using images with incomplete AU annotation but with complete expression labels. The goal is to use expression labels as hidden knowledge to complement the missing AU labels. Towards this goal, we propose to construct a Bayesian network (BN) to capture the relationships among facial expressions and AUs. Structural expectation maximization (SEM) is used to learn the structure and parameters of the BN when the AU labels are missing. Given the learned BNs and measurements of AUs and expression, we can then perform AU recognition within the BN through a probabilistic inference. Experimental results on the CK+, ISL and BP4D-Spontaneous databases demonstrate the effectiveness of our method for both AU classification and AU intensity estimation.