dc.creator | Paluzo Hidalgo, Eduardo | es |
dc.creator | González Díaz, Rocío | es |
dc.creator | Aguirre Carrazana, Guilermo | es |
dc.date.accessioned | 2022-06-30T11:27:15Z | |
dc.date.available | 2022-06-30T11:27:15Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Paluzo Hidalgo, E., González Díaz, R. y Aguirre Carrazana, G. (2022). Emotion recognition in talking-face videos using persistent entropy and neural networks. Electronic Research Archive, 30 (2), 644-660. | |
dc.identifier.issn | 2688-1594 | es |
dc.identifier.uri | https://hdl.handle.net/11441/134861 | |
dc.description.abstract | The automatic recognition of a person’s emotional state has become a very active research
field that involves scientists specialized in different areas such as artificial intelligence, computer vi sion, or psychology, among others. Our main objective in this work is to develop a novel approach,
using persistent entropy and neural networks as main tools, to recognise and classify emotions from
talking-face videos. Specifically, we combine audio-signal and image-sequence information to com pute a topology signature (a 9-dimensional vector) for each video. We prove that small changes in the
video produce small changes in the signature, ensuring the stability of the method. These topological
signatures are used to feed a neural network to distinguish between the following emotions: calm,
happy, sad, angry, fearful, disgust, and surprised. The results reached are promising and competitive,
beating the performances achieved in other state-of-the-art works found in the literature. | es |
dc.description.sponsorship | Agencia Estatal de Investigación PID2019-107339GB-100 | es |
dc.description.sponsorship | Agencia Andaluza del Conocimiento P20-01145 | es |
dc.format | application/pdf | es |
dc.format.extent | 17 | es |
dc.language.iso | eng | es |
dc.publisher | American Institute of Mathematical Sciences (AIMS) | es |
dc.relation.ispartof | Electronic Research Archive, 30 (2), 644-660. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Topological data analysis | es |
dc.subject | Persistent homology | es |
dc.subject | Persistent entropy | es |
dc.subject | Neural networks | es |
dc.subject | Audio-visual emotion recognition | es |
dc.subject | Talking-face videos | es |
dc.title | Emotion recognition in talking-face videos using persistent entropy and neural networks | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Matemática Aplicada I (ETSII) | es |
dc.relation.projectID | PID2019-107339GB-100 | es |
dc.relation.projectID | P20-01145 | es |
dc.relation.publisherversion | http://www.aimspress.com/article/doi/10.3934/era.2022034 | es |
dc.identifier.doi | 10.3934/era.2022034 | es |
dc.contributor.group | Universidad de Sevilla. FQM-369: Combinatorial Image Analysis | es |
dc.journaltitle | Electronic Research Archive | es |
dc.publication.volumen | 30 | es |
dc.publication.issue | 2 | es |
dc.publication.initialPage | 644 | es |
dc.publication.endPage | 660 | es |
dc.contributor.funder | Agencia Estatal de Investigación. España | es |
dc.contributor.funder | Agencia Andaluza del Conocimiento | es |