Deep Facial Action Unit Recognition from Partially Labeled Data

Current work on facial action unit (AU) recognition requires AU-labeled facial images. Although large amounts of facial images are readily available, AU annotation is expensive and time consuming. To address this, we propose a deep facial action unit recognition approach learning from partially AU-labeled data. The proposed approach makes full use of both partly available ground-truth AU labels and the readily available large scale facial images without annotation. Specifically, we propose to learn label distribution from the ground-truth AU labels, and then train the AU classifiers from the large-scale facial images by maximizing the log likelihood of the mapping functions of AUs with regard to the learnt label distribution for all training data and minimizing the error between predicted AUs and ground-truth AUs for labeled data simultaneously. A restricted Boltzmann machine is adopted to model AU label distribution, a deep neural network is used to learn facial representation from facial images, and the support vector machine is employed as the classifier. Experiments on two benchmark databases demonstrate the effectiveness of the proposed approach.