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dc.contributorLobo Quintero, René Alejandro-
dc.contributorhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001007017-
dc.contributorhttps://scholar.google.es/citations?hl=es#user=9vJhVRoAAAAJ-
dc.contributorhttps://orcid.org/0000-0003-2989-5357-
dc.contributorGrupo de Investigación Preservación e Intercambio Digital de Información y Conocimiento - Prisma-
dc.creatorJurado García, Miguel Eugenio-
dc.creatorPadilla Porras, Andrés Felipe-
dc.date2020-06-26T17:56:24Z-
dc.date2020-06-26T17:56:24Z-
dc.date2018-
dc.date.accessioned2022-03-14T20:14:00Z-
dc.date.available2022-03-14T20:14:00Z-
dc.identifierhttp://hdl.handle.net/20.500.12749/1315-
dc.identifierinstname:Universidad Autónoma de Bucaramanga - UNAB-
dc.identifierreponame:Repositorio Institucional UNAB-
dc.identifier.urihttp://biblioteca-repositorio.clacso.edu.ar/handle/CLACSO/22408-
dc.descriptionDebido 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.-
dc.description1. 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 59-
dc.descriptionPregrado-
dc.descriptionDue 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.-
dc.formatapplication/pdf-
dc.formatapplication/pdf-
dc.languagespa-
dc.publisherUniversidad Autónoma de Bucaramanga UNAB-
dc.publisherFacultad Ingeniería-
dc.publisherPregrado Ingeniería de Sistemas-
dc.relationPadilla Porras, Andrés Felipe (2018). Sistema de reconocimiento facial con redes neuronales para la toma de asistencia en aulas de clase. Bucaramanga (Colombia) : Universidad Autónoma de Bucaramanga UNAB-
dc.relationTalaviya, G., Ramteke, R., & Shete, A. (2013). Wireless Fingerprint Based College Attendance System Using Zigbee Technology. International Journal Of Engineering And Advanced Technology, (3), 201-203. Retrieved from https://pdfs.semanticscholar.org/a873/5eb75d3f1411798525fdc65875a8237b0c99.pdf-
dc.relationNawaz, T., Pervaiz, S., Korrani, A., & Ud-Din, A. (2009). Development of Academic Attendence Monitoring System Using Fingerprint Identification. International Journal Of Computer Science And Network Security, (9), 164-168. Retrieved from https://www.researchgate.net/profile/Tabassam_Nawaz/publication/242098052_Development_of_Academic_Attendence_Monitoring_System_Using_Fingerprint_Identification/links/5576abb008ae7521586c3c2b.pdf-
dc.relationMasalha, F., & Hirzallah, N. (2014). A Students Attendance System Using QR Code. International Journal Of Advanced Computer Science And Applications, (3), 75-79. Retrieved from https://thesai.org/Downloads/Volume5No3/Paper_10-A_Students_Attendance_System_Using_QR_Code.pdf-
dc.relationSajid, M., Hussain, R., & Usman, M. (2014). A conceptual Model For Automates Attendace Marking System Using Facial Recognition. Ninth International Conference on Digital Information Mangement (Págs. 7-10). Phitsanulok: IEEE.-
dc.relationMethi, D., Chauhan, A., & Gupta, D. (2017). Attendance System Using Face Recognition. International Journal Of Advanced Research In Science, Engineering And Technology, (4), 3897-3902. Retrieved from https://www.ijarset.com/upload/2017/may/11-IJARSET-DIVYAGUPTA.pdf-
dc.relationKawaguchi, Yohei & Shoji, Tetsuo. (2005). Face Recognition-based Lecture Attendance System, Retrieved from https://www.researchgate.net/publication/241608617_Face_Recognition-based_Lecture_Attendance_System-
dc.relationBalcoh, N., Yousaf, H., Ahmad, W., & Baig, I. (2012). Algorithm for Efficient Attendance Management: Face Recognition based approach. International Journal Of Computer Science Issues, 9(4), 146-150.-
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dc.relationShiffman, D. (2013). The Nature of Code. 1st ed. Shannon Fry, p.445.-
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dc.relationWhat is Zigbee?. (2014). Zigbee Alliance. Retrieved 23 September 2017, from http://www.zigbee.org/what-is-zigbee/-
dc.relationViola, P., & Jones, M. (2004). Robust Real-Time Face Detection. International Journal Of Computer Vision, 57(2), 137-154.-
dc.relationCireșan, D., Meier, U., Masci, J., Gambardella, L. and Schmidhuber, J. (2011). Flexible, High Performance Convolutional Neural Networks for Image Classification. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, [online] 2, pp.1237-1242. Available at: http://people.idsia.ch/~juergen/ijcai2011.pdf [Accessed 20 Sep. 2017].-
dc.relationGimeno Hernández, R. (2010). Estudio de técnicas de Reconocimiento facial.-
dc.relationRaspberry Pi. (2017). What is a Raspberry Pi?. [online] Available at: https://www.raspberrypi.org/help/what-%20is-a-raspberry-pi/ [Accessed 2 Oct. 2017].-
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dc.relationSimonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.-
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dc.relationShyam, R., & Singh, Y. N. (2014, December). Evaluation of eigenfaces and fisherfaces using bray curtis dissimilarity metric. In Industrial and Information Systems (ICIIS), 2014 9th International Conference on (pp. 1-6). IEEE.-
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dc.relationBedre, J. S., & Sapkal, S. (2012). Comparative Study of Face Recognition Techniques: A Review. Emerging Trends in Computer Science and Information Technology–2012 (ETCSIT2012) Proceedings published in International Journal of Computer Applications®(IJCA), 12.-
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dc.relationVirdee-Chapman, B. (2017). Face Recognition: Kairos vs Microsoft vs Google vs Amazon vs OpenCV. Kairos. Retrieved 30 October 2017, from https://www.kairos.com/blog/face-recognition-kairos-vs-microsoft-vs-google-vs-amazon-vs-opencv-
dc.relationHan, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques (3rd ed.). Elsevier.-
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dc.relationClassification Performance Metrics - NLP-FOR-HACKERS. (2018). Retrieved from https://nlpforhackers.io/classification-performance-metrics/-
dc.relationAtribución-NoComercial-SinDerivadas 2.5 Colombia-
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/2.5/co/-
dc.rightsAbierto (Texto Completo)-
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.rightshttp://purl.org/coar/access_right/c_abf2-
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia-
dc.subjectPerception of faces-
dc.subjectFacial recognition-
dc.subjectNeural Networks-
dc.subjectComputers-
dc.subjectArtificial intelligence-
dc.subjectSystems Engineering-
dc.subjectInvestigations-
dc.subjectAnalysis-
dc.subjectArtificial vision-
dc.subjectAutomation-
dc.subjectNeural networks-
dc.subjectArtificial intelligence-
dc.subjectPercepción de caras-
dc.subjectReconocimiento facial-
dc.subjectRedes neuronales-
dc.subjectComputadores-
dc.subjectInteligencia artificial-
dc.subjectIngeniería de sistemas-
dc.subjectInvestigaciones-
dc.subjectAnálisis-
dc.subjectInteligencia artificial-
dc.subjectRedes neuronales-
dc.subjectAutomatización-
dc.subjectVisión artificial-
dc.titleSistema de reconocimiento facial con redes neuronales para la toma de asistencia en aulas de clase-
dc.titleFacial recognition system with neural networks for taking assistance in classrooms-
dc.typeinfo:eu-repo/semantics/bachelorThesis-
dc.typeTrabajo de Grado-
dc.typehttp://purl.org/coar/resource_type/c_7a1f-
dc.typeinfo:eu-repo/semantics/acceptedVersion-
dc.typehttp://purl.org/redcol/resource_type/TP-
dc.coverageBucaramanga (Colombia)-
Aparece en las colecciones: Instituto de Estudios Políticos - IEP - Cosecha

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