Comunicació presentada a: IEEE International Conference on Computer Vision (ICCV 2015) celebrat a Santiago, Xile, de l'11 al 18 de desembre de 2015.

Limited annotated training data is a challenging problem in Action Unit recognition. In this paper, we investigate how the use of large databases labelled according to the 6 universal facial expressions can increase the generalization ability of Action Unit classifiers. For this purpose, we propose a novel learning framework: Hidden-Task Learning. HTL aims to learn a set of Hidden-Tasks (Action Units) for which samples are not available but, in contrast, training data is easier to obtain from a set of related Visible- Tasks (Facial Expressions). To that end, HTL is able to exploit prior knowledge about the relation between Hidden and Visible-Tasks. In our case, we base this prior knowledge on empirical psychological studies providing statistical correlations between Action Units and universal facial expressions. Additionally, we extend HTL to Semi-Hidden Task Learning (SHTL) assuming that Action Unit training samples are also provided. Performing exhaustive experiments over four different datasets, we show that HTL and SHTL improve the generalization ability of AU classifiers by training them with additional facial expression data. Additionally, we show that SHTL achieves competitive performance compared with state-of-the-art Transductive Learning approaches which face the problem of limited training data by using unlabelled test samples during training.

This paper is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 645012. Adria Ruiz and Xavier Binefa would also like to acknowledge Spanish Government to provide support under grants CICYT TIN2012-39203 and FPU13/01740. Joost van de Weijer acknowledges Project TIN2013-41751 of the Spanish Ministry of Science and the Generalitat de Catalunya Project under Grant 2014-SGR-221.