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dc.contributor.advisorPardo Beainy, Camilospa
dc.contributor.advisorRodríguez Caro, Danielspa
dc.contributor.authorPardo Morcote, Julián Davidspa
dc.date.accessioned2020-10-20T17:11:47Zspa
dc.date.available2020-10-20T17:11:47Zspa
dc.date.issued2020-10-17spa
dc.identifier.citationPardo, J. (2020). Reconocimiento facial en tiempo real orientado a video llamadas o live stream para autenticar identidades durante una audiencia legal Tesis Ingeniería Electrónica. Universidad Santo Tomas Tunjaspa
dc.identifier.urihttp://hdl.handle.net/11634/30508spa
dc.descriptionEn este libro se encuentra la descripción del diseño y desarrollo de la aplicación para reconocimiento facial en tiempo real, se documenta todo el desarrollo de esta herramienta con alta calidad investigativa y buenos resultados en las pruebas realizadas, con el fin de aportar una solución a la problemática de suplantación de identidades, estableciendo como objetivo crear una herramienta informática para autenticar identidades durante video llamadas, que se construyó gracias a la herramienta DLIB una librería que ayuda a detectar objetos, en este caso el rostro de una persona. Se implementó el método ingenieril con enfoque aplicativo ya que no cualifica ni cuantifica variables y en conclusión se obtiene un software especializado en reconocimiento biométrico facial de alta fiabilidad, convirtiendo a este libro en una fuente de consulta seria y organizada que aporta conocimiento a la academia y a los ingenieros electrónicos que deseen realizar una investigación relacionada con reconocimiento biométrico facial.spa
dc.description.abstractIn this book you will find the description of the design and development of the application for facial recognition in real time, the entire development of this tool is documented with high investigative quality and good results in the tests carried out, in order to provide a solution to the problem of identity theft, establishing the objective of creating a IT tool to authenticate identities during video calls, which was built thanks to the DLIB tool, a library that helps to detect objects, in this case the face of a person. The engineering method with an applicative approach was implemented since it does not qualify or quantifies variables and in conclusion a specialized recognition software is obtained highly reliable facial biometric, making this book a source for serious and organized that brings knowledge to academia and electronic engineers who want conduct research related to biometric facial recognition.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.publisherUniversidad Santo Tomásspa
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.titleReconocimiento facial en tiempo real orientado a video llamadas o live stream para autenticar identidades durante una audiencia legalspa
dc.description.degreenameIngeniero Electronicospa
dc.publisher.programPregrado Ingeniería Electrónicaspa
dc.publisher.facultyFacultad de Ingeniería Electrónicaspa
dc.subject.keywordfacial recognitionspa
dc.subject.keywordDlibspa
dc.subject.keywordneural networksspa
dc.subject.keywordartificial visionspa
dc.subject.keywordartificial intelligencespa
dc.subject.keywordPythonspa
dc.type.localTrabajo de gradospa
dc.rights.localAbierto (Texto Completo)spa
dc.type.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.rights.accessrightshttp://purl.org/coar/access_right/c_abf2spa
dc.coverage.campusCRAI-USTA Tunjaspa
dc.relation.referencesPresente y futuro del reconocimiento facial. (n.d.). Retrieved June 16, 2020, from https://www.padigital.es/tendencias-digitales/historia-y-evolucion-del-reconocimiento-facial.htmlspa
dc.relation.referencesDlib C++ Library: High Quality Face Recognition with Deep Metric Learning. (n.d.). Retrieved June 16, 2020, from http://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.htmlspa
dc.relation.referencesMachine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning. (n.d.). Retrieved June 16, 2020, from https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-facerecognition-with-deep-learning-c3cffc121d78spa
dc.relation.referencesKetkar, N. (2017). Deep Learning with Python. In Deep Learning with Python. https://doi.org/10.1007/978- 1-4842-2766-4spa
dc.relation.referencesLutz, M. (2007). Learning Python. In Icarus. https://doi.org/10.1016/0019-1035(89)90077-8spa
dc.relation.referencesBradski, G., & Kaehler, A. (2008). Learning OpenCV. In Learning OpenCV. https://doi.org/10.1109/MRA.2009.933612spa
dc.relation.referencesZelinsky, A. (2009). Learning OpenCV—Computer Vision with the OpenCV Library. In IEEE Robotics and Automation Magazine. https://doi.org/10.1109/MRA.2009.933612spa
dc.relation.referencesProgramming python. (1997). Computers & Mathematics with Applications. https://doi.org/10.1016/s0898- 1221(97)82952-5spa
dc.relation.referencesHowse, J. (2013). OpenCV Computer Vision with Python. In Cs_Python_in. https://doi.org/10.1017/CBO9781107415324.004spa
dc.relation.referencesItseez. (2012). OpenCV - Open Source Computer Vision. Online Webpage.spa
dc.relation.referencesXie, Z., Li, J., & Shi, H. (2019). A Face Recognition Method Based on CNN. Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/1395/1/012006spa
dc.relation.referencesYaswanth, V. V. S., & Devi, T. (2019). Automatic emotion recognition using facial expression by python. Test Engineering and Management.spa
dc.relation.referencesADIEGO RODRIGUEZ, J. (2007). books google. Obtenido de https://books.google.es/books?id=FfEfCBhXCgC&pg=PT297&dq=base+datos+relacional+codd&hl=es&sa=X&ved=0ahUKEwjixf LYxcPXAhXMWhQKHQImAfUQ6AEIQjAF#v=onepage&q=base%20datos%20relacio nal%20codd&f=falsespa
dc.relation.referencesAmos, B. (septiembre de 2016). ResearchGate. Obtenido de ResearchGate: https://www.researchgate.net/figure/The-68-landmarks-detected-by-dlib-library-Thisimage-was-created-by-Brandon-Amos-of-CMU_fig2_329392737spa
dc.relation.referencesArce, J. I. (26 de julio de 2019). health Big Data. Obtenido de https://www.juanbarrios.com/matrizde-confusion-y-sus-metricas/spa
dc.relation.referencesBoyko, N., Basystiuk, O., & Shakhovska, N. (2018). Performance Evaluation and Comparison of Software for Face Recognition, Based on Dlib and Opencv Library. IEEE explore.spa
dc.relation.referencescontabal. (2016). Obtenido de https://www.contaval.es/que-es-la-vision-artificial-y-para-quesirve/spa
dc.relation.referencesDuque, R. G. (2020). Python para todos. En R. G. Duque, Python para todos.spa
dc.relation.referencesGeitgey, A. (2017). Readthedocs. Obtenido de Readthedocs: https://facerecognition.readthedocs.io/en/latest/face_recognition.htmlspa
dc.relation.referencesGuay, M. (2010). how to geek. Obtenido de https://www.howtogeek.com/howto/17528/changethe-user-interface-language-in-ubuntu/spa
dc.relation.referencesInovation, A. (2019). Atria Inovation. Obtenido de https://www.atriainnovation.com/que-son-lasredes-neuronales-y-sus-funciones/spa
dc.relation.referencesKing, D. (2017). http://blog.dlib.net/. Obtenido de http://blog.dlib.net/: http://blog.dlib.net/spa
dc.relation.referencesNa8. (28 de noviembre de 2019). como funcionan las convolutional neural networks? Obtenido de aprende machine learnign: https://www.aprendemachinelearning.com/como-funcionanlas-convolutional-neural-networks-vision-por-ordenador/spa
dc.relation.referencesNetCloud. (2019). Obtenido de https://netcloudengineering.com/funcionamiento-redes-lan/spa
dc.relation.referencesOpencv.org. (2020). Opencv. Obtenido de Opencv: https://opencv.org/about/spa
dc.relation.referencesPADigital. (27 de noviembre de 2018). padigital.es. Obtenido de https://www.padigital.es/tendencias-digitales/historia-y-evolucion-del-reconocimientofacial.html#:~:text=Los%20inicios%20del%20reconocimiento%20facial,trav%C3%A9s %20de%20la%20tabla%20RAND.spa
dc.relation.referencesQuintana, J. (mayo de 2018). medium.com. Obtenido de medium.com: https://medium.com/datosy-ciencia/modelos-cnn-en-la-clasificaci%C3%B3n-de-im%C3%A1genescl%C3%A1sicas-y-modernas-d072a6718689spa
dc.relation.referencesRajesh, K. M., & Naveenkumar, M. (2016). A robust method for face recognition and face emotion detection system using support vector machines. IEEE explore.spa
dc.relation.referencesRivera, M. (Agosto de 2019). resnet.md. Obtenido de resnet.md: http://personal.cimat.mx:8181/~mrivera/cursos/aprendizaje_profundo/resnet/resnet.htmlspa
dc.relation.referencesRussell, S. J., & Norvig, P. N. (2009). Artificial intelligence: a modern approach. En S. J. Russell, & P. N. Norvig.spa
dc.relation.referencesSampieri, R. (2014). Metodología de la investigación 6ta Edición. McGRAW-HILL / INTERAMERICANA EDITORES, S.A. DE C.V.spa
dc.relation.referencessignificados.com. (2020). Obtenido de https://www.significados.com/software/spa
dc.relation.referencesSphinx. (2019). https://pyautogui.readthedocs.io/en/latest/. Obtenido de https://pyautogui.readthedocs.io/en/latest/: https://pyautogui.readthedocs.io/en/latest/spa
dc.relation.referencestecnologia informatica. (2020). Obtenido de https://www.tecnologia-informatica.com/algoritmodefinicion/spa
dc.relation.referencesThilaga, P. J., Khan, B. A., Jones, A., & Kumar, N. K. (2018). Modern Face Recognition with Deep Learning. IEEE explore.spa
dc.relation.referenceszator.com. (2020). Obtenido de zator.spa
dc.subject.proposalreconocimiento facialspa
dc.subject.proposalDlibspa
dc.subject.proposalredes neuronalesspa
dc.subject.proposalvisión artificialspa
dc.subject.proposalinteligencia artificialspa
dc.subject.proposalPythonspa
dc.identifier.reponamereponame:Repositorio Institucional Universidad Santo Tomásspa
dc.identifier.instnameinstname:Universidad Santo Tomásspa
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1fspa
dc.description.degreelevelPregradospa
dc.identifier.repourlrepourl:https://repository.usta.edu.cospa


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