Publication

Biometric lock with facial recognition implemented with deep learning techniques

Journal Article (2024)

Journal

Computer Science and Information Systems

Pages

1359-1387

Volume

21

Number

4

Doc link

https://doi.org/10.2298/CSIS240229038B

File

Download the digital copy of the doc pdf document

Authors

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.

Categories

control theory.

Author keywords

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

Scientific reference

J.M. Burruel-Zazueta, H. Rodríguez, G.E. Peralta-Penunuri, V. Puig, I. Algredo-Badillo and L.A. Morales-Rosales. Biometric lock with facial recognition implemented with deep learning techniques. Computer Science and Information Systems, 21(4): 1359-1387, 2024.