Sistema de acceso usando una tarjeta RFiD y verificación de rostro

Main Article Content

José Ignacio Vega Luna http://orcid.org/0000-0002-4226-2936
Francisco Javier Sánchez-Rangel https://orcid.org/0000-0002-4182-5856
Gerardo Salgado-Guzmán https://orcid.org/0000-0002-0581-7410
Mario Alberto Lagos-Acosta https://orcid.org/0000-0003-0455-007X

Abstract

En este trabajo se presenta el desarrollo de un prototipo de sistema de acceso a un centro de datos usando como identificación una tarjeta de radio frecuencia o RFiD y verificación del rostro del usuario. El sistema se compone de tres módulos de entrada y un módulo central. El objetivo fue diseñar un sistema para transmitir, desde cada módulo de entrada al módulo central, el identificador único universal de la tarjeta RFiD o UUID y la imagen del rostro del usuario para consultar en una base de datos MySQL y en un directorio de fotografías si el usuario puede acceder al área correspondiente del módulo de entrada. Cada módulo de entrada consta de una tarjeta Raspberry Pi 3 B+, un lector de tarjetas RFiD, una cámara de video y una pantalla de cristal líquido o LCD. El módulo central se compone de los mismos elementos que los módulos de entrada y cuenta con una pantalla táctil usada en la interfaz de usuario en lugar de una pantalla LCD. La comunicación entre los nodos es wifi, logrando una precisión del 99,2 % en la verificación del rostro y un tiempo de respuesta de 180 ms usando 310 fotografías entrenadas.
 
Abstract 599 | PDF (Español (España)) Downloads 993 PDF Downloads 380

References

[1] M. V. M. Lima, R. M. F. Lima, and F. A. A. Lins, “A multi-perspective methodology for evaluating the security maturity of data centers,” in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Oct 2017. doi: https://doi.org/10.1109/SMC.2017.8122775, pp. 1196–1201.
[2] M. Levy and J. O. Hallstrom, “A new approach to data center infrastructure monitoring and management (dcimm),” in 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), Jan 2017. doi: https://doi.org/10.1109/CCWC.2017.7868412, pp. 1–6.
[3] I. B. Mustaffa and S. F. B. M. Khairul, “Identification of fruit size and maturity through fruit images using opencv-python and rasberry pi,” in 2017 International Conference on Robotics, Automation and Sciences (ICORAS), Nov 2017. doi: https://doi.org/10.1109/ICORAS.2017.8308068, pp. 1–3.
[4] J. Mihal’ov and M. Hulic, “Nfc/rfid technology using raspberry pi as platform used in smart home project,” in 2017 IEEE 14th International Scientific Conference on Informatics, Nov 2017. doi: https://doi.org/10.1109/INFORMATICS.2017.8327257, pp. 259–264.
[5] N. Goel, A. Sharma, and S. Goswami, “A way to secure a qr code: Sqr,” in 2017 International Conference on Computing, Communication and Automation (ICCCA), May 2017. doi: https://doi.org/10.1109/CCAA.2017.8229850, pp. 494–497.
[6] S. Menon, A. George, N. Mathew, V. Vivek, and J. John, “Smart workplace – using ibeacon,” in 2017 International Conference on Networks Advances in Computational Technologies (NetACT), July 2017. doi: https://doi.org/10.1109/NETACT.2017.8076803, pp. 396–400.
[7] X. Li, D. Xu, X. Wang, and R. Muhammad, “Design and implementation of indoor positioning system based on ibeacon,” in 2016 International Conference on Audio, Language and Image Processing (ICALIP), July 2016. doi: https://doi.org/10.1109/ICALIP.2016.7846648, pp. 126–130.
[8] M. Chamekh, S. E. Asmi, M. Hamdi, and T. H. Kim, “Context aware middleware for rfid based pharmaceutical supply chain,” in 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), June 2017. doi: https://doi.org/10.1109/IWCMC.2017.7986576, pp. 1915–1920.
[9] K. B. Eric and W. H. Ya, “Iot based smart restaurant system using rfid,” in 4th International Conference on Smart and Sustainable City (ICSSC 2017), June 2017. doi: https://doi.org/10.1049/cp.2017.0123, pp. 1–6.
[10] M. Andriansyah, M. Subali, I. Purwanto, S. A. Irianto, and R. A. Pramono, “e-ktp as the basis of home security system using arduino uno,” in 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT), Aug 2017. doi: https://doi.org/10.1109/CAIPT.2017.8320693, pp. 1–5.
[11] S. Nath, P. Banerjee, R. N. Biswas, S. K. Mitra, and M. K. Naskar, “Arduino based door unlocking system with real time control,” in 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Dec 2016. doi: https://doi.org/10.1109/IC3I.2016.7917989, pp.358–362.
[12] J. Cui, D. She, J. Ma, Q. Wu, and J. Liu, “A new logistics distribution scheme based on nfc,” in 2015 International Conference on Network and Information Systems for Computers, Jan 2015. doi: https://doi.org/10.1109/ICNISC.2015.48, pp. 492–495.
[13] W. Xiao-Long, W. Chun-Fu, L. Guo-Dong, and C. Qing-Xie, “A robot navigation method based on rfid and qr code in the warehouse,” in 2017 Chinese Automation Congress (CAC), Oct 2017. doi: https://doi.org/10.1109/CAC.2017.8244199, pp. 7837–7840.
[14] H. Keni, M. Earle, and M. Min, “Product authentication using hash chains and printed qr codes,” in 2017 14th IEEE Annual Consumer Communications Networking Conference (CCNC), Jan 2017. doi: https://doi.org/10.1109/CCNC.2017.7983126, pp. 319–324.
[15] P. Pramkeaw, T. Ganokratanaa, and S. Phatchuay, “Integration of watermarking and qr code for authentication of data center,” in 2016 12th International Conference on Signal-Image Technology Internet-Based Systems (SITIS), Nov 2016. doi: https://doi.org/10.1109/SITIS.2016.111, pp. 669–672.
[16] H. Zou, Z. Chen, H. Jiang, L. Xie, and C. Spanos, “Accurate indoor localization and tracking using mobile phone inertial sensors, wifi and ibeacon,” in 2017 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), March 2017. doi: https://doi.org/10.1109/ISISS.2017.7935650, pp. 1–4.
[17] Z. Yu, F. Liu, R. Liao, Y. Wang, H. Feng, and X. Zhu, “Improvement of face recognition algorithm based on neural network,” in 2018 10th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Feb 2018. doi: https://doi.org/10.1109/ICMTMA.2018.00062, pp. 229–234.
[18] N. Mokoena, H. D. Tsague, and A. Helberg, “2d methods for pose invariant face recognition,” in 2016 International Conference on Computational Science and Computational Intelligence (CSCI), Dec 2016. doi: https://doi.org/10.1109/CSCI.2016.0163, pp. 841–846. [19] D. Goldman. (2015) Microsoft will let you unlock windows 10 with your face. CNN tech. [Online]. Available: https://goo.gl/tgo8pM
[20] F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015. doi: https://doi.org/10.1109/CVPR.2015.7298682, pp. 815–823.
[21] S. Srisuk and S. Ongkittikul, “Robust face recognition based on weighted deepface,” in 2017 International Electrical Engineering Congress (iEECON), March 2017. doi: https://doi.org/10.1109/IEECON.2017.8075885, pp. 1–4.
[22] M. Wiglasz and L. Sekanina, “Evolutionary approximation of gradient orientation module in hogbased human detection system,” in 2017 IEEE
Global Conference on Signal and Information Processing (GlobalSIP), Nov 2017. doi: https://doi.org/10.1109/GlobalSIP.2017.8309171, pp. 1300–1304.
[23] J. Zeng, X. Zhao, C. Qin, and Z. Lin, “Single sample per person face recognition based on deep convolutional neural network,” in 2017 3rd IEEE International Conference on Computer and Communications (ICCC), Dec 2017. doi: https://doi.org/10.1109/CompComm.2017.8322819, pp. 1647–1651.
[24] X. Chen, L. Qing, X. He, J. Su, and Y. Peng, “From eyes to face synthesis: a new approach for human-centered smart surveillance,” IEEE Access, vol. 6, pp. 14 567–14 575, 2018. doi: https://doi.org/10.1109/ACCESS.2018.2803787.
[25] A. H. M. Amin, N. M. Ahmad, and A. M. M. Ali, “Decentralized face recognition scheme for distributed video surveillance in iot-cloud infrastructure,” in 2016 IEEE Region 10 Symposium (TENSYMP), May 2016. doi: https://doi.org/10.1109/TENCONSpring.2016.7519389, pp. 119–124.
[26] S. Karahan and Y. S. Akgül, “Eye detection by using deep learning,” in 2016 24th Signal Processing and Communication Application Conference (SIU), May 2016. doi: https://doi.org/10.1109/SIU.2016.7496197, pp. 2145–2148.