A Smart Locking System Based on Multimodal Biometrics Authentication

In this research, we address the security concerns associated with traditional physical key-based locking systems, such as the risk of key duplication and loss. To mitigate these issues, we propose the use of biometric authentication systems, which offer enhanced security by leveraging unique biological traits that cannot be replicated or lost. This study explores the implementation of three biometric verification methods: fingerprint recognition, facial recognition, and RFID (Radio Frequency Identification). The system integrates a fingerprint sensor, which captures the user's fingerprint and transmits it to the motherboard for matching against stored records. Facial recognition is achieved through a webcam that captures the user's image, which is then processed and analyzed. Additionally, RFID scanning is conducted using a coil antenna that reads the relevant data. Upon successful biometric verification, the system triggers a green light to indicate authentication success, while a red light is displayed in case of a mismatch. This paper demonstrates the use of green and red lights as indicators for authentication outcomes, elimlnating the need for traditional locks.

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Publication Details

Published
2024-10-04
DOI
https://doi.org/10.1109/i4c62240.2024.10748442
Citations
1
Primary Topic
Vehicle License Plate Recognition
Type
article
Field-Weighted Citation Impact
0.32
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article

A Smart Locking System Based on Multimodal Biometrics Authentication

Tapan Kumar Das, Ravi Shekhar Tiwari, G Durgashri, Arati Mohapatro
1 citations
Vehicle License Plate Recognition
0.32
article

A Smart Locking System Based on Multimodal Biometrics Authentication

Tapan Kumar Das, Ravi Shekhar Tiwari, G Durgashri, Arati Mohapatro
article en
1 citations

Abstract

In this research, we address the security concerns associated with traditional physical key-based locking systems, such as the risk of key duplication and loss. To mitigate these issues, we propose the use of biometric authentication systems, which offer enhanced security by leveraging unique biological traits that cannot be replicated or lost. This study explores the implementation of three biometric verification methods: fingerprint recognition, facial recognition, and RFID (Radio Frequency Identification). The system integrates a fingerprint sensor, which captures the user's fingerprint and transmits it to the motherboard for matching against stored records. Facial recognition is achieved through a webcam that captures the user's image, which is then processed and analyzed. Additionally, RFID scanning is conducted using a coil antenna that reads the relevant data. Upon successful biometric verification, the system triggers a green light to indicate authentication success, while a red light is displayed in case of a mismatch. This paper demonstrates the use of green and red lights as indicators for authentication outcomes, elimlnating the need for traditional locks.

Mahindra Group (India) (IN), Vellore Institute of Technology University (IN)
Openalex Percentile: Top 35%
Vehicle License Plate Recognition
0.32
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