Facial recognition system for access control through Artificial Intelligence

Keywords: Access Control, Artificial Intelligence, Convolutional Neural Networks

Abstract

The main objective of this article is the development of a system that allows the facial recognition of a person for access control through Artificial Intelligence. For the development of the system, the Convolutional Neural Networks algorithm was used, which is a recognition model. Likewise, the Python programming language and the following libraries such as Numpy, Os, OpenCV and Imutils were used for its implementation. The results obtained according to the hit and using a dataset of 4500 images are approximately 88% in terms of the prediction per person, concluding that the recognition system is effective and has greater efficiency by increasing the size of datasets generated by individuals.

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References

"Facial Expression Recognition Using Machine Learning Techniques", International Journal of Advance Engineering and Research Development, vol. 1, n.º 06, junio de 2020. Accedido el 17 de noviembre de 2022. [En línea]. Disponible: https://doi.org/10.21090/ijaerd.010633

R. F. Rahmat, E. N. Zai, I. Fawwaz y I. Aulia, "Facial Recognition-Based Automatic Door Access System Using Extreme Learning Machine", IOP Conference Series: Materials Science and Engineering, vol. 851, p. 012065, mayo de 2020. Accedido el 17 de noviembre de 2022. [En línea]. Disponible: https://doi.org/10.1088/1757-899x/851/1/012065

"Librería NumPy - Aprende IA". Aprende IA. https://aprendeia.com/libreria-de-python-numpy-machine-learning/ (accedido el 17 de noviembre de 2022).

"os - Interfaces miscelíneas del sistema operativo — documentación de Python - 3.10.8". 3.11.0 Documentation. https://docs.python.org/es/3.10/library/os.html (accedido el 17 de noviembre de 2022).

"OpenCV: OpenCV modules". OpenCV documentation index. https://docs.opencv.org/4.x/ (accedido el 17 de noviembre de 2022).

"¿Qué es Python? | Guía de Python para principiantes de la nube | AWS". Amazon Web Services, Inc. https://aws.amazon.com/es/what-is/python/ (accedido el 17 de noviembre de 2022).

Anuja Jadhav, Yash Joshi y Vishakha Kalambe, "Face Based Attendance System Using Convolutional Neural Network", International Journal of Advanced Research in Science, Communication and Technology, pp. 51–54, febrero de 2022. Accedido el 17 de noviembre de 2022. [En línea]. Disponible: https://doi.org/10.48175/ijarsct-2506

O. Niel y P. Bastard, "Artificial Intelligence in Nephrology: Core Concepts, Clinical Applications, and Perspectives", American Journal of Kidney Diseases, vol. 74, n.º 6, pp. 803–810, diciembre de 2019. Accedido el 17 de noviembre de 2022. [En línea]. Disponible: https://doi.org/10.1053/j.ajkd.2019.05.020

T. Walsh, "The troubling future for facial recognition software", Communications of the ACM, vol. 65, n.º 3, pp. 35–36, marzo de 2022. Accedido el 15 de noviembre de 2022. [En línea]. Disponible: https://doi.org/10.1145/3474096

M. Smith y S. Miller, "The ethical application of biometric facial recognition technology", AI & SOCIETY, abril de 2021. Accedido el 17 de noviembre de 2022. [En línea]. Disponible: https://doi.org/10.1007/s00146-021-01199-9

S. Latifi, Ed., 17th International Conference on Information Technology–New Generations (ITNG 2020). Cham: Springer International Publishing, 2020. Accedido el 17 de noviembre de 2022. [En línea]. Disponible: https://doi.org/10.1007/978-3-030-43020-7

S. K. Shammi, S. Sultana, M. S. Islam y A. Chakrabarty, "Low Latency Image Processing of Transportation System Using Parallel Processing co-incident Multithreading (PPcM)", en 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, Japan, 25–29 de junio de 2018. IEEE, 2018. Accedido el 17 de noviembre de 2022. [En línea]. Disponible: https://doi.org/10.1109/iciev.2018.8640957

M. Nicho, S. Khan y M. S. M. K. Rahman, "Managing Information Security Risk Using Integrated Governance Risk and Compliance", en 2017 International Conference on Computer and Applications (ICCA), Doha, United Arab Emirates, 6–7 de septiembre de 2017. IEEE, 2017. Accedido el 17 de noviembre de 2022. [En línea]. Disponible: https://doi.org/10.1109/comapp.2017.8079741

S. Rasnayaka, S. Saha y T. Sim, "Making the most of what you have! Profiling biometric authentication on mobile devices", en 2019 International Conference on Biometrics (ICB), Crete, Greece, 4–7 de junio de 2019. IEEE, 2019. Accedido el 17 de noviembre de 2022. [En línea]. Disponible: https://doi.org/10.1109/icb45273.2019.8987402

I. Sluganovic, M. Roeschlin, K. B. Rasmussen y I. Martinovic, "Analysis of Reflexive Eye Movements for Fast Replay-Resistant Biometric Authentication", ACM Transactions on Privacy and Security, vol. 22, n.º 1, pp. 1–30, enero de 2019. Accedido el 17 de noviembre de 2022. [En línea]. Disponible: https://doi.org/10.1145/3281745

L. Monastyrskii, V. Lozynskii, Y. Boyko y B. Sokolovskii, "Fingerprint recognition in inexpensive biometric system", Electronics and Information Technologies, vol. 9, 2018. Accedido el 17 de noviembre de 2022. [En línea]. Disponible: https://doi.org/10.30970/eli.9.120

Received: 2022-09-22
Accepted: 2022-11-05
Published: 2023-03-30
How to Cite
[1]
J. E. M. Reyes Campos, C. S. Castañeda Rodríguez, L. D. Alva Luján, and A. C. Mendoza de los Santos, “Facial recognition system for access control through Artificial Intelligence”, Innov. softw., vol. 4, no. 1, pp. 24-36, Mar. 2023.
Section
Journal papers

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