Latent-based adversarial neural networks for facial affect estimations

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• dc.contributor.author Aspandi, Decky
• dc.contributor.author Mallol Ragolta, Adrià
• dc.contributor.author Schuller, Björn
• dc.contributor.author Binefa i Valls, Xavier
• dc.date.accessioned 2021-03-26T09:31:56Z
• dc.date.issued 2020
• dc.description Comunicació presentada al 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), celebrat del 16 al 20 de novembre de 2020 a Buenos Aires, Argentina.
• dc.description.abstract There is a growing interest in affective computing research nowadays given its crucial role in bridging humans with computers. This progress has recently been accelerated due to the emergence of bigger dataset. One recent advance in this field is the use of adversarial learning to improve model learning through augmented samples. However, the use of latent features, which is feasible through adversarial learning, is not largely explored, yet. This technique may also improve the performance of affective models, as analogously demonstrated in related fields, such as computer vision. To expand this analysis, in this work, we explore the use of latent features through our proposed adversarial-based networks for valence and arousal recognition in the wild. Specifically, our models operate by aggregating several modalities to our discriminator, which is further conditioned to the extracted latent features by the generator. Our experiments on the recently released SEWA dataset suggest the progressive improvements of our results. Finally, we show our competitive results on the Affective Behavior Analysis in-the-Wild (ABAW) challenge dataset.en
• dc.description.sponsorship This work is partly supported by the Spanish Ministry of Economy and Competitiveness under project grant TIN2017- 90124-P, the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502), and the donation bahi2018-19 to the CMTech at UPF. Further funding has been received from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 826506 (sustAGE).
• dc.format.mimetype application/pdf
• dc.identifier.citation Aspandi D, Mallol-Ragolta A, Schuller B, Binefa X. Latent-based adversarial neural networks for facial affect estimations. In: Štruc V, Gómez-Fernández F, editors. 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020); 2020 Nov 16-20; Buenos Aires, Argentina. New Jersey: IEEE; 2020. p. 606-10. DOI: 10.1109/FG47880.2020.00053
• dc.identifier.doi http://dx.doi.org/10.1109/FG47880.2020.00053
• dc.identifier.uri http://hdl.handle.net/10230/46962
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof Štruc V, Gómez-Fernández F, editors. 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020); 2020 Nov 16-20; Buenos Aires, Argentina. New Jersey: IEEE; 2020. p. 606-10
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/TIN2017-90124-P
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/MDM-2015-0502
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/826506
• dc.rights © 2020 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. http://dx.doi.org/10.1109/FG47880.2020.00053
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Feature extractionen
• dc.subject.keyword Computational modelingen
• dc.subject.keyword Trainingen
• dc.subject.keyword Generatorsen
• dc.subject.keyword Estimationen
• dc.subject.keyword Computer visionen
• dc.subject.keyword Biological system modelingen
• dc.title Latent-based adversarial neural networks for facial affect estimationsen
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion