This paper presents a novel approach to secure door access control utilizing a combination of face recognition, Li-Fi technology, and smartphone-based authentication. The proposed system leverages the advantages of Li-Fi for high-speed, secure data transmission, along with the convenience of face recognition and smartphone authentication. The system comprises an ESP32-CAM module for face recognition, an Arduino microcontroller for controlling the door lock mechanism, and Li-Fi transceivers for data communication between the smartphone and the system.
Introduction
Traditional door lock systems often rely on physical keys or access cards, which can be easily lost or stolen. Moreover, these methods lack the flexibility and security offered by modern technological advancements. To address these limitations, this paper proposes a novel door lock system that integrates face recognition, Li-Fi technology, and smartphone authentication.
Li-Fi Technology: Li-Fi, or Light Fidelity, is a wireless communication technology that uses visible light to transmit data. It offers several advantages over traditional Wi-Fi, including higher data rates, improved security, and immunity to radio frequency interference.
Face Recognition: Face recognition technology has gained significant popularity in recent years due to its accuracy and convenience. By analyzing facial features, the system can identify authorized users and grant access accordingly.
Smartphone Authentication: Smartphone-based authentication provides an additional layer of security. By transmitting a unique code or pattern using Li-Fi, users can further verify their identity and authorize access.
System Design and Implementation
Hardware Components:
ESP32-CAM: This versatile microcontroller board with a built-in camera module is used for face recognition and Li-Fi data transmission.
Arduino Microcontroller: This microcontroller is responsible for controlling the door lock mechanism and interfacing with the ESP32-CAM.
Li-Fi Transceivers: These devices convert electrical signals into light signals and vice versa, enabling high-speed data transmission.
Door Lock Mechanism: This can be a solenoid lock, a motor-driven lock, or any other suitable mechanism.
Software Components:
Face Recognition Algorithm: A robust face recognition algorithm, such as Haar Cascade or Deep Learning-based methods, is implemented on the ESP32-CAM to identify authorized users.
Li-Fi Communication Protocol: A custom Li-Fi communication protocol is developed to ensure reliable and secure data transmission between the smartphone and the system.
Smartphone App: A smartphone app is developed to control the Li-Fi transmitter and input the user's authentication credentials.
System Operation:
- Face Recognition: The ESP32-CAM captures a live image of the user's face and compares it with stored templates. If a match is found, the system unlocks the door.
- Smartphone Authentication:
— The user launches the smartphone app and enters a password or biometric authentication.
— The app generates a unique code or pattern.
— The code or pattern is encoded into a Li-Fi signal and transmitted from the smartphone's flashlight.
— The ESP32-CAM receives the Li-Fi signal and decodes the authentication information.
— If the authentication is successful, the system unlocks the door.
In the face recognition process, the ESP32-CAM module plays a pivotal role. It captures a live image of the user's face and compares it against pre-stored facial templates within its database. This comparison is executed using sophisticated algorithms that can accurately identify authorized users. If a match is detected, the system initiates the unlocking mechanism, allowing access. This method enhances security by ensuring that only recognized individuals can gain entry.
The smartphone authentication process adds an additional layer of security. Upon launching the dedicated smartphone application, users are required to enter either a password or utilize biometric verification methods such as fingerprint scanning or facial recognition. The application then generates a unique code or pattern that is encoded into a Li-Fi signal using the smartphone's flashlight. This innovative use of light for data transmission not only facilitates high-speed communication but also mitigates risks associated with traditional wireless methods, such as eavesdropping.
Results
The system design incorporates several key components: the ESP32-CAM module for face recognition, an Arduino microcontroller for controlling the lock mechanism, and Li-Fi transceivers for data exchange between the smartphone and the locking system. The face recognition algorithm employed can be based on advanced techniques such as deep learning, which has demonstrated significant accuracy in identifying individuals. Additionally, the smartphone authentication process adds another layer of security by requiring users to input a unique code transmitted via Li-Fi.
In terms of implementation, the paper outlines a systematic approach where the user’s face is captured and analyzed in real-time. If recognized, access is granted; otherwise, the system prompts for smartphone authentication. This dual-factor authentication method not only enhances security but also ensures that unauthorized users are effectively barred from accessing secured areas.
Future work suggested by the authors includes refining the system's accuracy and speed while exploring additional security features like biometric authentication and encryption methods. This research contributes significantly to the field of smart security systems and has potential applications in both residential and commercial environments, addressing the growing need for advanced security solutions in an increasingly digital world.
Conclusion
This paper presents a novel and secure door lock system that combines the advantages of face recognition, Li-Fi technology, and smartphone authentication. The system offers a high level of security and convenience, making it suitable for various applications, including residential and commercial settings. Future work may involve improving the system's accuracy and speed, as well as exploring additional security features, such as biometric authentication and encryption.
References:
- Kaushal, R., & Singh, P. (2016). Li-Fi: A novel approach for high-speed wireless communication. International Journal of Computer Applications, 142(11), 1–6.
- Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014). DeepFace: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp.1701–1708).
- Garg, D., & Kumar, S. (2018). Internet of Things (IoT): A review. International Journal of Computer Applications, 179(47), 1–11.
