Escalera Guerrero, SergioPalmero Cantariño, CristinaJacques Junior, Julio C. S.Masdeu Ninot, Andreu2020-05-192020-05-192019-09-02https://hdl.handle.net/2445/161200Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2019, Tutor: Sergio Escalera Guerrero, Cristina Palmero i Julio C. S. Jacques Junior[en] In this thesis we explore the use of Multitask Learning for improving performance in facial attributes tasks such as gender, age and ethnicity prediction. These tasks, along with emotion recognition will be part of a new dyadic interaction dataset which was recorded during the development of this thesis. This work includes the implementation of two state of the art multitask deep learning models and the discussion of the results obtained from these methods in a preliminary dataset, as well as a first evaluation in a sample of the dyadic interaction dataset. This will serve as a baseline for a future implementation of Multitask Learning methods in the fully annotated dyadic interaction dataset.63 p.application/pdfengcc-by-sa (c) Andreu Masdeu Ninot, 2019codi: GPL (c) Andreu Masdeu Ninot, 2019http://creativecommons.org/licenses/by-sa/3.0/es/http://www.gnu.org/licenses/gpl-3.0.ca.htmlAprenentatge automàticEmocionsTreballs de fi de màsterExpressió facialMachine learningEmotionsMaster's thesesFacial expressioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccess