Please use this identifier to cite or link to this item:
http://hdl.handle.net/10553/134753
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chaudhari, Aayushi | en_US |
dc.contributor.author | Bhatt, Chintan | en_US |
dc.contributor.author | Krishna, Achyut | en_US |
dc.contributor.author | Travieso González, Carlos Manuel | en_US |
dc.date.accessioned | 2024-11-19T19:34:26Z | - |
dc.date.available | 2024-11-19T19:34:26Z | - |
dc.date.issued | 2023 | en_US |
dc.identifier.issn | 2079-9292 | en_US |
dc.identifier.uri | http://hdl.handle.net/10553/134753 | - |
dc.description.abstract | Emotion recognition is a very challenging research field due to its complexity, as individual differences in cognitive–emotional cues involve a wide variety of ways, including language, expressions, and speech. If we use video as the input, we can acquire a plethora of data for analyzing human emotions. In this research, we use features derived from separately pretrained self-supervised learning models to combine text, audio (speech), and visual data modalities. The fusion of features and representation is the biggest challenge in multimodal emotion classification research. Because of the large dimensionality of self-supervised learning characteristics, we present a unique transformer and attention-based fusion method for incorporating multimodal self-supervised learning features that achieved an accuracy of 86.40% for multimodal emotion classification. | en_US |
dc.language | eng | en_US |
dc.relation.ispartof | Electronics (Switzerland) | en_US |
dc.source | Electronics (Switzerland) [ISSN 2079-9292], v. 12 (2), 288, (Enero 2023) | en_US |
dc.subject | 120325 Diseño de sistemas sensores | en_US |
dc.subject | 610603 Emoción | en_US |
dc.subject.other | Computer vision | en_US |
dc.subject.other | Contextual emotion recognition | en_US |
dc.subject.other | Depth of emotional dimensionality | en_US |
dc.subject.other | Inter-modality attention transformer | en_US |
dc.subject.other | Multimodality | en_US |
dc.subject.other | Real-time application | en_US |
dc.subject.other | Self-attention transformer | en_US |
dc.title | Facial Emotion Recognition with Inter-Modality-Attention-Transformer-Based Self-Supervised Learning | en_US |
dc.type | info:eu-repo/semantics/article | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.3390/electronics12020288 | en_US |
dc.identifier.scopus | 2-s2.0-85146763720 | - |
dc.contributor.orcid | 0000-0003-0342-8806 | - |
dc.contributor.orcid | 0000-0002-0423-0159 | - |
dc.contributor.orcid | 0000-0003-4504-9636 | - |
dc.contributor.orcid | 0000-0002-4621-2768 | - |
dc.identifier.issue | 2 | - |
dc.relation.volume | 12 | en_US |
dc.investigacion | Ingeniería y Arquitectura | en_US |
dc.type2 | Artículo | en_US |
dc.description.numberofpages | 15 | en_US |
dc.utils.revision | Sí | en_US |
dc.date.coverdate | Enero 2023 | en_US |
dc.identifier.ulpgc | Sí | en_US |
dc.contributor.buulpgc | BU-TEL | en_US |
dc.description.sjr | 0,644 | |
dc.description.jcr | 2,9 | |
dc.description.sjrq | Q2 | |
dc.description.jcrq | Q2 | |
dc.description.scie | SCIE | |
dc.description.miaricds | 10,5 | |
item.grantfulltext | open | - |
item.fulltext | Con texto completo | - |
crisitem.author.dept | GIR IDeTIC: División de Procesado Digital de Señales | - |
crisitem.author.dept | IU para el Desarrollo Tecnológico y la Innovación | - |
crisitem.author.dept | Departamento de Señales y Comunicaciones | - |
crisitem.author.orcid | 0000-0002-4621-2768 | - |
crisitem.author.parentorg | IU para el Desarrollo Tecnológico y la Innovación | - |
crisitem.author.fullName | Travieso González, Carlos Manuel | - |
Appears in Collections: | Artículos |
Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.