Access Control for Al-Iman Workshop using Facial Recognition System

Abstract

Abstract
Unauthorized Access Alert System addresses security vulnerabilities during non-operational hours by integrating face recognition technology with Raspberry Pi and a Flutter admin application for real-time monitoring and immediate alerts. Traditional security measures, such as manual surveillance and physical locks, often fail to prevent unauthorized access, particularly when tools and equipment are left outside, making establishments like the Al-Iman Center, a car repair shop, susceptible to intrusions and potential threats. To mitigate these limitations, our system employs Deep Learning algorithms in facial recognition, seamlessly integrated with Raspberry Pi for processing and detection. The user-friendly Flutter admin application is linked to Google Firebase, enabling real-time monitoring and rapid alerts. Upon detecting intruders, the system captures images and alerts authorities instantly. Our methodology includes using advanced facial recognition technology to identify unauthorized individuals, Raspberry Pi for local processing, and a responsive Flutter application for real-time monitoring and alerts. This approach significantly enhances security, reduces response time, and safeguards assets during non-operational hours. The system's significance lies in its proactive prevention of security breaches, emphasizing advanced technology for safety and protection. It is applicable in various settings, including car care shops, offices, industrial facilities, or any establishment vulnerable to unauthorized access during non-operational hours, thereby bolstering security protocols and improving real-time monitoring to minimize risks and safeguard valuable assets.