Year: 2025
Type: article
Abstract: : Traditional security measures often fail to provide accurate and efficient monitoring, leading to vulnerabilities in restricted areas. This project implements an advanced real-time facial recognition-based surveillance system to enhance security and address these shortcomings. The system uses facial recognition technology for live monitoring, identifying authorized personnel and detecting unauthorized individuals. Attendance is recorded automatically in an Excel file, with timestamps for entry and exit. For unauthorized access, the system triggers multiple actions, including capturing and saving the individual's image, uploading it to Azure Blob Storage, sending email alerts with timestamps and image URLs, and activating a buzzer alarm alongside an on-screen warning. By integrating automated facial recognition with real-time alerts and cloud storage, the system significantly improves surveillance accuracy and efficiency, providing a robust solution for preventing unauthorized access in restricted areas.
Author: Jahith Sai Krishna Varma
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410469267