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

Jurado García, Miguel Eugenio; Padilla Porras, Andrés Felipe

DSpace Principal
→
Colección de los Centros Asociados
→
Colombia
→
Instituto de Estudios Políticos - IEP - Cosecha
→
Ver ítem

dc.contributor	Lobo Quintero, René Alejandro
dc.contributor	https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001007017
dc.contributor	https://scholar.google.es/citations?hl=es#user=9vJhVRoAAAAJ
dc.contributor	https://orcid.org/0000-0003-2989-5357
dc.contributor	Grupo de Investigación Preservación e Intercambio Digital de Información y Conocimiento - Prisma
dc.creator	Jurado García, Miguel Eugenio
dc.creator	Padilla Porras, Andrés Felipe
dc.date	2020-06-26T17:56:24Z
dc.date	2020-06-26T17:56:24Z
dc.date	2018
dc.date.accessioned	2022-03-14T20:14:00Z
dc.date.available	2022-03-14T20:14:00Z
dc.identifier	http://hdl.handle.net/20.500.12749/1315
dc.identifier	instname:Universidad Autónoma de Bucaramanga - UNAB
dc.identifier	reponame:Repositorio Institucional UNAB
dc.identifier.uri	http://biblioteca-repositorio.clacso.edu.ar/handle/CLACSO/22408
dc.description	Debido 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.description	1. 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.description	Pregrado
dc.description	Due 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.format	application/pdf
dc.format	application/pdf
dc.language	spa
dc.publisher	Universidad Autónoma de Bucaramanga UNAB
dc.publisher	Facultad Ingeniería
dc.publisher	Pregrado Ingeniería de Sistemas
dc.relation	Padilla 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.relation	Talaviya, 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.relation	Nawaz, 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.relation	Masalha, 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.relation	Sajid, 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.relation	Methi, 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.relation	Kawaguchi, 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.relation	Balcoh, 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.
dc.relation	Qrcode.com. (2017). History of QR Code \| QRcode.com \| DENSO WAVE. [online] Available at: http://www.qrcode.com/en/history/ [Accessed 21 Sep. 2017].
dc.relation	Shiffman, D. (2013). The Nature of Code. 1st ed. Shannon Fry, p.445.
dc.relation	Rouse, M. (2015). Framework. whatis. Retrieved 23 September 2017, from http://whatis.techtarget.com/definition/framework
dc.relation	What is Zigbee?. (2014). Zigbee Alliance. Retrieved 23 September 2017, from http://www.zigbee.org/what-is-zigbee/
dc.relation	Viola, P., & Jones, M. (2004). Robust Real-Time Face Detection. International Journal Of Computer Vision, 57(2), 137-154.
dc.relation	Cireșan, 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.relation	Gimeno Hernández, R. (2010). Estudio de técnicas de Reconocimiento facial.
dc.relation	Raspberry Pi. (2017). What is a Raspberry Pi?. [online] Available at: https://www.raspberrypi.org/help/what-%20is-a-raspberry-pi/ [Accessed 2 Oct. 2017].
dc.relation	Abdallah, A. S., Abbott, A. L., & El-Nasr, M. A. (2007, May). A new face detection technique using 2D DCT and self organizing feature map. In Proc. of World Academy of Science, Engineering and Technology (Vol. 21, pp. 15-19)
dc.relation	The Database of Faces. (2002). The Database of Faces. [online] Available at: http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html [Accessed 5 Sep. 2017]
dc.relation	Belongie, S., Malik, J., & Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE transactions on pattern analysis and machine intelligence, 24(4), 509-522
dc.relation	Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on pattern analysis and machine intelligence, 19(7), 711-720.
dc.relation	Adini, Y., Moses, Y., & Ullman, S. (1997). Face recognition: The problem of compensating for changes in illumination direction. IEEE Transactions on pattern analysis and machine intelligence, 19(7), 721-732.
dc.relation	Phillips, P. J. (1999). Support vector machines applied to face recognition. In Advances in Neural Information Processing Systems(pp. 803-809).
dc.relation	Liu, C., & Wechsler, H. (2000). Evolutionary pursuit and its application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(6), 570-582.
dc.relation	Gordon, G. G. (1991, September). Face recognition based on depth maps and surface curvature. In Geometric Methods in Computer Vision (Vol. 1570, pp. 234-248). International Society for Optics and Photonics.
dc.relation	Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997). Face recognition: A convolutional neural-network approach. IEEE transactions on neural networks, 8(1), 98-113.
dc.relation	Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
dc.relation	Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016). Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830.
dc.relation	Shyam, 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.
dc.relation	Redmon, J., & Angelova, A. (2015, May). Real-time grasp detection using convolutional neural networks. In Robotics and Automation (ICRA), 2015 IEEE International Conference on (pp. 1316-1322). IEEE.
dc.relation	Hongxun, Y., Wen, G., Mingbao, L., & Lizhuang, Z. (2000). Eigen features technique and its application. In Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on (Vol. 2, pp. 1153-1158). IEEE.
dc.relation	Bedre, 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.
dc.relation	Heiselet, B., Serre, T., Pontil, M., & Poggio, T. (2001). Component-based face detection. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on (Vol. 1, pp. I-I). IEEE.
dc.relation	Virdee-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.relation	Han, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques (3rd ed.). Elsevier.
dc.relation	Han, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques (3rd ed.). Elsevier.
dc.relation	Classification Performance Metrics - NLP-FOR-HACKERS. (2018). Retrieved from https://nlpforhackers.io/classification-performance-metrics/
dc.relation	Atribución-NoComercial-SinDerivadas 2.5 Colombia
dc.rights	http://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.rights	Abierto (Texto Completo)
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	http://purl.org/coar/access_right/c_abf2
dc.rights	Atribución-NoComercial-SinDerivadas 2.5 Colombia
dc.subject	Perception of faces
dc.subject	Facial recognition
dc.subject	Neural Networks
dc.subject	Computers
dc.subject	Artificial intelligence
dc.subject	Systems Engineering
dc.subject	Investigations
dc.subject	Analysis
dc.subject	Artificial vision
dc.subject	Automation
dc.subject	Neural networks
dc.subject	Artificial intelligence
dc.subject	Percepción de caras
dc.subject	Reconocimiento facial
dc.subject	Redes neuronales
dc.subject	Computadores
dc.subject	Inteligencia artificial
dc.subject	Ingeniería de sistemas
dc.subject	Investigaciones
dc.subject	Análisis
dc.subject	Inteligencia artificial
dc.subject	Redes neuronales
dc.subject	Automatización
dc.subject	Visión artificial
dc.title	Sistema de reconocimiento facial con redes neuronales para la toma de asistencia en aulas de clase
dc.title	Facial recognition system with neural networks for taking assistance in classrooms
dc.type	info:eu-repo/semantics/bachelorThesis
dc.type	Trabajo de Grado
dc.type	http://purl.org/coar/resource_type/c_7a1f
dc.type	info:eu-repo/semantics/acceptedVersion
dc.type	http://purl.org/redcol/resource_type/TP
dc.coverage	Bucaramanga (Colombia)