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Sistema de reconocimiento facial con redes neuronales para la toma de asistencia en aulas de clase

dc.contributor.advisorLobo Quintero, René Alejandrospa
dc.contributor.authorJurado García, Miguel Eugeniospa
dc.contributor.authorPadilla Porras, Andrés Felipespa
dc.date.accessioned2020-06-26T17:56:24Z
dc.date.available2020-06-26T17:56:24Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/20.500.12749/1315
dc.description.abstractDebido al aumento de estudiantes en la universidad y el gran tamaño de los cursos, en especial los de cátedra de la facultad de medicina, se evidencia la necesidad de agilizar el proceso de toma de asistencia de los estudiantes y docentes. En este trabajo se especifican los requerimientos de un sistema de reconocimiento facial para la toma de asistencia automatizada en aulas de clase basado en redes neuronales convolucionales y se muestran resultados del desempeño del sistema en un aula de clase de la Universidad Autónoma de Bucaramanga.spa
dc.description.tableofcontents1. INTRODUCCIÓN 4 2. PLANTEAMIENTO DEL PROBLEMA 5 3. PLANTEAMIENTO DE LA SOLUCIÓN 6 4. OBJETIVOS 8 4.1. OBJETIVO GENERAL 8 4.2. OBJETIVOS ESPECIFICOS 8 5. RESULTADOS ESPERADOS 8 5.1. Objetivo específico 1 8 5.2. Objetivo específico 2 8 5.3. Objetivo específico 3 8 5.4. Objetivo específico 4 9 5.5. Objetivo específico 5 9 6. ESTADO DEL ARTE 10 7. MARCO TEORICO 22 7.1. Framework 22 7.2. Red neuronal 22 7.3. CNN 22 7.4. Darknet 23 7.5. Código QR 23 7.6. Zigbee 24 7.7. Minucia 24 7.8. Haar Features 24 7.9. Viola Jones 26 7.10. PCA (Principal Component Analysis) 26 7.11. LDA (Linear Discriminant Analysis) 27 7.12. DCT (Discrete Cosine Transform) por bloques 27 7.13. Raspberry 28 8. METODOLOGÍA 29 9. RESULTADOS OBTENIDOS 31 9.1. Objetivo específico 1 31 9.2. Objetivo específico 2 36 9.3. Objetivo específico 3 39 9.4. Objetivo específico 4 44 9.5. Objetivo específico 5 45 10. Conclusiones 53 11. REFERENCIAS 56 12. Anexos 59spa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.titleSistema de reconocimiento facial con redes neuronales para la toma de asistencia en aulas de clasespa
dc.title.translatedFacial recognition system with neural networks for taking assistance in classroomseng
dc.degree.nameIngeniero de Sistemasspa
dc.coverageBucaramanga (Colombia)spa
dc.publisher.grantorUniversidad Autónoma de Bucaramanga UNABspa
dc.rights.localAbierto (Texto Completo)spa
dc.publisher.facultyFacultad Ingenieríaspa
dc.publisher.programPregrado Ingeniería de Sistemasspa
dc.description.degreelevelPregradospa
dc.type.driverinfo:eu-repo/semantics/bachelorThesis
dc.type.localTrabajo de Gradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.subject.keywordsPerception of faceseng
dc.subject.keywordsFacial recognitioneng
dc.subject.keywordsNeural Networkseng
dc.subject.keywordsComputerseng
dc.subject.keywordsArtificial intelligenceeng
dc.subject.keywordsSystems Engineeringeng
dc.subject.keywordsInvestigationseng
dc.subject.keywordsAnalysiseng
dc.subject.keywordsArtificial visioneng
dc.subject.keywordsAutomationeng
dc.subject.keywordsNeural networkseng
dc.subject.keywordsArtificial intelligenceeng
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga - UNABspa
dc.identifier.reponamereponame:Repositorio Institucional UNABspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.accessrightshttp://purl.org/coar/access_right/c_abf2spa
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dc.relation.referencesAtribución-NoComercial-SinDerivadas 2.5 Colombiaspa
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001007017*
dc.contributor.googlescholarhttps://scholar.google.es/citations?hl=es#user=9vJhVRoAAAAJ*
dc.contributor.orcidhttps://orcid.org/0000-0003-2989-5357*
dc.subject.lembPercepción de carasspa
dc.subject.lembReconocimiento facialspa
dc.subject.lembRedes neuronalesspa
dc.subject.lembComputadoresspa
dc.subject.lembInteligencia artificialspa
dc.subject.lembIngeniería de sistemasspa
dc.subject.lembInvestigacionesspa
dc.subject.lembAnálisisspa
dc.description.abstractenglishDue to the increase in students at the university and the large size of the courses, especially those of the faculty of medicine, the need to speed up the process of taking attendance of students and teachers is evident. In this work, the requirements of a facial recognition system for automated attendance taking in classrooms based on convolutional neural networks are specified and results of the performance of the system in a classroom of the Universidad Autónoma de Bucaramanga are shown.eng
dc.subject.proposalInteligencia artificial
dc.subject.proposalRedes neuronales
dc.subject.proposalAutomatización
dc.subject.proposalVisión artificial
dc.type.redcolhttp://purl.org/redcol/resource_type/TP
dc.rights.creativecommonsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.contributor.researchgroupGrupo de Investigación Preservación e Intercambio Digital de Información y Conocimiento - Prismaspa
dc.contributor.researchgroupGrupo de Investigaciones Clínicasspa
dc.coverage.campusUNAB Campus Bucaramangaspa
dc.description.learningmodalityModalidad Presencialspa


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