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dc.contributor.authorÁlvarez, Víctor M.
dc.contributor.authorSánchez-Gómez, Claudia
dc.contributor.authorGutiérrez, Sebastián
dc.contributor.authorDomínguez Soberanes, Julieta
dc.contributor.authorVelázquez, Ramiro
dc.contributor.otherCampus Aguascalientes
dc.date.accessioned2019-01-22T16:58:19Z
dc.date.available2019-01-22T16:58:19Z
dc.date.issued2018
dc.identifier.citationÁlvarez, V. M., Sánchez Gómez, C. N., Gutiérrez Calderón, J. S. Dominguez-Soberanes, J. y Velázquez Guerrero, R. (2018). Facial emotion recognition: a comparison of different Landmark-based Classifiers. En: 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE), San Salvador, 2018, (pp. 1-4). DOI: 10.1109/RICE.2018.8509048es_ES, en_US
dc.identifier.isbn9781538625996es_ES, en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12552/4803
dc.identifier.urihttp://dx.doi.org/10.1109/RICE.2018.8509048
dc.description.abstractAs stated by Ekman in his Facial Action Coding System (FACS), facial expressions can be interpreted as the activation of different sets of facial muscles. This recognition skill, however, demands extensive training and is indeed time consuming. Consequently, there have been many attempts to automate this process. In this paper, after applying common face detection and alignment algorithms to the Cohn-Kanade dataset, we fed a group of emotion-labeled landmarks to different classifiers in order to compare their results. The multilayer perceptron classifier showed the best performance with an average accuracy of 89%. © 2018 IEEE.es_ES, en_US
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.es_ES, en_US
dc.relationVersión del editores_ES, en_US
dc.relation.ispartofREPOSITORIO SCRIPTAes_ES, en_US
dc.relation.ispartofOPENAIREes_ES, en_US
dc.rightsAcceso Cerradoes_ES, en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0es_ES, en_US
dc.sourceProceedings of the 2018 3rd IEEE International Conference on Research in Intelligent and Computing in Engineering, RICE 2018
dc.subjectClassifieres_ES, en_US
dc.subjectFacial expression recognitiones_ES, en_US
dc.subjectLandmark detectiones_ES, en_US
dc.subjectMultilayer perceptrones_ES, en_US
dc.subjectSupervised learning techniqueses_ES, en_US
dc.subjectClassification (of information)es_ES, en_US
dc.subjectComputer keyboardses_ES, en_US
dc.subjectIntelligent computinges_ES, en_US
dc.subjectMultilayer neural networkses_ES, en_US
dc.subjectMultilayerses_ES, en_US
dc.subjectSupervised learninges_ES, en_US
dc.subjectAlignment algorithmses_ES, en_US
dc.subjectFacial action coding systemes_ES, en_US
dc.subjectFacial emotionses_ES, en_US
dc.subjectFacial expression recognitiones_ES, en_US
dc.subjectFacial expressionses_ES, en_US
dc.subjectFacial muscleses_ES, en_US
dc.subjectLandmark detectiones_ES, en_US
dc.subjectMulti-layer perceptron classifierses_ES, en_US
dc.subjectFace recognitiones_ES, en_US
dc.subject.classificationINGENIERÍA Y TECNOLOGÍAes_ES, en_US
dc.subject.classificationCIENCIAS SOCIALES
dc.subject.classificationIngeniería
dc.subject.classificationAdministración de Instituciones
dc.titleFacial emotion recognition : a comparison of different landmark-based classifierses_ES, en_US
dc.typeContribución a congresoes_ES, en_US
dcterms.audienceInvestigadores
dcterms.audienceEstudiantes
dcterms.audienceMaestros
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