A multi-modal emotion recogniser based on the integration of multiple fusion methods

Simple item page
dc.contributor.advisorTicona Herrera, Regina Paola
dc.contributor.authorHeredia Parillo, Juanpablo Andrew
dc.date.accessioned2021-11-29T03:04:43Z
dc.date.available2021-11-29T03:04:43Z
dc.date.issued2021
dc.description.abstractPeople naturally express emotions in simultaneous different ways. Thus, multimodal methods are becoming popular for emotion recognition and analysis of reactions to many aspects of daily life. This research work presents a multimodal method for emotion recognition from images. The multi-modal method analyses facial expressions, body gestures and the characteristics of the body and the environment to determine an emotional state, processing each modality with a specialised deep learning model and then applying the proposed fusion method. The fusion method, called EmbraceNet+, consists of a branched architecture that integrates the EmbraceNet fusion method with other fusion methods. The tests carried out on an adaptation of the EMOTIC dataset show that the proposed multi-modal method is effective and improves the results obtained by individual processings, as well as competing with other state-ofthe-art methods. The proposed method has many areas of application because it seeks to recognise emotions in any situation. Likewise, the proposed fusion method can be used in any multi-modal deep learning-based model.es_PE
dc.description.uriTesises_PE
dc.formatapplication/pdfes_PE
dc.identifier.other1073589
dc.identifier.urihttps://hdl.handle.net/20.500.12590/16940
dc.language.isoenges_PE
dc.publisherUniversidad Católica San Pabloes_PE
dc.publisher.countryPEes_PE
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/es_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.subjectEmotion recognitiones_PE
dc.subjectMulti-modal Methodes_PE
dc.subjectMultiple Fusion Methodses_PE
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#1.02.01es_PE
dc.titleA multi-modal emotion recogniser based on the integration of multiple fusion methodses_PE
dc.typeinfo:eu-repo/semantics/bachelorThesis
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_PE
renati.advisor.dni40207170
renati.advisor.orcidhttps://orcid.org/0000-0002-2605-5718es_PE
renati.author.dni75151804
renati.discipline611016es_PE
renati.jurorJose Eduardo Ochoa Lunaes_PE
renati.jurorYessenia Deysi Yari Ramoses_PE
renati.levelhttps://purl.org/pe-repo/renati/level#tituloProfesional
renati.typehttps://purl.org/pe-repo/renati/type#tesis
thesis.degree.disciplineCiencia de la Computaciónes_PE
thesis.degree.grantorUniversidad Católica San Pablo. Departamento de Ciencia de la Computaciónes_PE
thesis.degree.levelTítulo Profesionales_PE
thesis.degree.nameLicenciado en Ciencia de la Computaciónes_PE
thesis.degree.programPrograma Profesional de Ciencia de la Computaciónes_PE
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
HEREDIA_PARILLO_JUA_EMO.pdf
Size:
5.56 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Tesis - Ciencias de la Computación