Geodesic
Biometric lock with facial recognition implemented with deep learning techniques
Computer Science and Information Systems, Tome 21 (2024) no. 4.

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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.
Keywords: FaceNet, Jetson Nano, CNN, Door lock, Embedded system 
@article{CSIS_2024_21_4_a10,
     author = {Jos\'e Misael Burruel-Zazueta and H\'ector Rodr{\'\i}guez-Rangel and Gloria Ekaterine Peralta-Pe\~nu\~nuri and Vicen\c{c} Puig Cayuela and Ignacio Algredo-Badillo and Luis Alberto Morales-Rosales},
     title = {Biometric lock with facial recognition implemented with deep learning techniques},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {21},
     number = {4},
     year = {2024},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2024_21_4_a10/}
}
TY  - JOUR
AU  - José Misael Burruel-Zazueta
AU  - Héctor Rodríguez-Rangel
AU  - Gloria Ekaterine Peralta-Peñuñuri
AU  - Vicenç Puig Cayuela
AU  - Ignacio Algredo-Badillo
AU  - Luis Alberto Morales-Rosales
TI  - Biometric lock with facial recognition implemented with deep learning techniques
JO  - Computer Science and Information Systems
PY  - 2024
VL  - 21
IS  - 4
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/CSIS_2024_21_4_a10/
ID  - CSIS_2024_21_4_a10
ER  - 
%0 Journal Article
%A José Misael Burruel-Zazueta
%A Héctor Rodríguez-Rangel
%A Gloria Ekaterine Peralta-Peñuñuri
%A Vicenç Puig Cayuela
%A Ignacio Algredo-Badillo
%A Luis Alberto Morales-Rosales
%T Biometric lock with facial recognition implemented with deep learning techniques
%J Computer Science and Information Systems
%D 2024
%V 21
%N 4
%I mathdoc
%U http://geodesic.mathdoc.fr/item/CSIS_2024_21_4_a10/
%F CSIS_2024_21_4_a10
José Misael Burruel-Zazueta; Héctor Rodríguez-Rangel; Gloria Ekaterine Peralta-Peñuñuri; Vicenç Puig Cayuela; Ignacio Algredo-Badillo; Luis Alberto Morales-Rosales. Biometric lock with facial recognition implemented with deep learning techniques. Computer Science and Information Systems, Tome 21 (2024) no. 4. http://geodesic.mathdoc.fr/item/CSIS_2024_21_4_a10/