Facial action unit detection using kernel partial least squares

In this work, we propose a framework for simultaneously detecting the presence of multiple facial action units using kernel partial least square regression (KPLS). This method has the advantage of being easily extensible to learn more face related labels, while at the same time being computationally efficient. We compare the approach to linear and non-linear support vector machines (SVM) and evaluate its performance on the extended Cohn-Kanade (CK+) dataset and the GEneva Multimodal Emotion Portrayals (GEMEP-FERA) dataset, as well as across databases. It is shown that KPLS achieves around 2% absolute improvement over the SVM-based approach in terms of the two alternative forced choice (2AFC) score when trained on CK+ and tested on CK+ and GEMEP-FERA. It achieves around 6% absolute improvement over the SVM-based approach when trained on GEMEP-FERA and tested on CK+. We also show that KPLS is handling non-additive AU combinations better than SVM-based approaches trained to detect single AUs only.