Anti-Theft Flooring System using Facial Recognition

Aniket Bhoyar*, Gulrukh Nazneen**, Ritu Pawar***, Vaishnavi Shinde****, Vaishnavi Tajane*****, Premlata Sonewane******, Siddhesh Hude*******
*-******* Department of Computer Science and Engineering, S. B. Jain Institute of Technology, Management and Research, Nagpur, India.
Periodicity:July - December'2024

Abstract

This study introduces a new IoT solution for an anti-theft flooring system that uses facial recognition technology to enhance security in homes and workspaces. It comprises sensors, such as piezoelectric pressure sensors placed on the floor, which are sensitive to pressure or movement. When detection occurs, a nearby camera is triggered to capture the individual's face, which is then matched against a facial database using a facial recognition feature. If the face is matched successfully, a four-digit One-Time Password (OTP) is generated by the system, and a GSM module sends it to the registered mobile number. The individual must then enter the correct OTP through a Bluetooth-enabled mobile application governed by the HC-05 module. If the correct OTP is entered before the 60-second timer expires, the system unlocks the secured area using a 12V DC electromagnetic lock. If the OTP is incorrect or the timer expires, the secured area remains locked, and the system displays "Invalid OTP" on the LCD screen. This solution enhances existing security measures, such as motion sensors and cameras, which can be bypassed using tricks like IR cloaks or by exploiting blind spots. Pressure detection, facial recognition, and OTP-based two-factor authentication are employed in the proposed system to monitor the area in real-time, provide access control to officially prescribed personnel, and respond quickly to any unauthorized attempts. This method offers robust security at a low cost and can be effortlessly scaled to many homes, businesses, and highly secured zones.

Keywords

Facial Recognition, Piezo Sensors, One-Time Password (OTP), GSM Module, Bluetooth Module (HC-05), Arduino Uno, Electromagnetic Lock.

How to Cite this Article?

Bhoyar, A., Nazneen, G., Pawar, R., Shinde, V., Tajane, V., Sonewane, P., and Hude, S. (2024). Anti-Theft Flooring System using Facial Recognition. i-manager’s Journal on IoT and Smart Automation, 2(2), 1-12.

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