Ruiz, AdriàVan de Weijer, JoostBinefa i Valls, Xavier2018-03-282018-03-282015Ruiz A, Van de Weiejer J, Binefa X. From emotions to action units with hidden and semi-hidden-task learning. In: Proceedings 2015 IEEE International Conference on Computer Vision (ICCV 2015); 2015 Dec 11–18; Santiago, Chile. [New York]: IEEE; 2015. p. 3703-11. DOI: 10.1109/ICCV.2015.4222380-7504http://hdl.handle.net/10230/34269Comunicació 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.application/pdfeng© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The final published article can be found at http://ieeexplore.ieee.org/document/7410779/info:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/ICCV.2015.422TrainingGoldTraining dataFace recognitionDatabasesPsychologyHidden Markov modelsinfo:eu-repo/semantics/openAccess