Biometric lock with facial recognition implemented with deep learning techniques

José Misael Burruel-Zazueta1, Héctor Rodríguez-Rangel1, Gloria Ekaterine Peralta-Peñuñuri1, Vicenç Puig Cayuela2, Ignacio Algredo-Badillo3 and Luis Alberto Morales-Rosales4

  1. Tecnológico Nacional de México/Instituto Tecnológico de Culiacán
    Juan de Dios Batiz No. 310 pte, Guadalupe, 80220 Culiacán Rosales, Sin., México
    d13170395@culiacan.tecnm.mx, hector.rr@culiacan.tecnm.mx, gloria.pp@culiacan.tecnm.mx
  2. Institut de Robòtica i Informàtica Industrial, CSIC-UPC
    C/ Llorens i Artigas 4-6, 08028 Barcelona, España
    vicenc.puig@upc.edu
  3. CONACYT-Instituto Nacional de Astrofísica, Óptica y Electrónica
    Tonantzintla 72840, Puebla, México
    algredobadillo@inaoep.mx
  4. CONACYT-Universidad Michoacana de San Nicolás de Hidalgo
    Avenida Francisco J. Múgica S/N Ciudad Universitaria, Morelia, México
    lamorales@conacyt.mx

Abstract

The increased criminal activity associated with unauthorized entry into facilities has become a global concern. Traditional mechanical locks suffer from drawbacks such as key loss, theft, duplication risks, and time-consuming operation. Therefore, biometrics has been explored as a key to accessing a restricted area. However, some challenges still need to be solved in developing such systems, including user registration, response speed, maintainability, and the ability to distinguish between real and fake individuals. This paper proposes and develops a biometric lock system (BLS) whose opening is performed by recognizing a person’s face. It solves the challenges of re-training, antispoofing, real-time response, and works inside an embedding system. The proposed BLS overcomes these challenges using a pre-trained network called FaceNet for feature extraction and coding into 128-dimensional vectors. We use the characteristic vector provided by FaceNet and a cosine distance to recognize the persons. It also incorporates ResNet18 + remote photoplethysmography (rPPG) to avoid spoofing. The architecture was implemented in a BLS, demonstrating an impressive false acceptance rate of 0% under varying lighting conditions, with an average response time of 1.68 seconds from facial detection to door opening. The BLS has easy maintainable devices, providing enhanced security by accurately identifying individuals and preventing unauthorized access. The system can distinguish between real and fake people without using specialized hardware. Making it a versatile solution suitable for homes, offices, and commercial spaces. The results underscore the potential efficacy of our proposed BLS in mitigating security challenges related to unwarranted access to restricted facilities.

Key words

FaceNet, Jetson Nano, CNN, Door lock, Embedded system

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS240229038B

Publication information

Volume 21, Issue 4 (September 2024)
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Burruel-Zazueta, J. M., Rodríguez-Rangel, H., Peralta-Peñuñuri, G. E., Cayuela, V. P., Algredo-Badillo, I., Morales-Rosales, L. A.: Biometric lock with facial recognition implemented with deep learning techniques. Computer Science and Information Systems, Vol. 21, No. 4, 1359–1387. (2024), https://doi.org/10.2298/CSIS240229038B