VSaaS
AI Box
VMS
Intrusion Detection
AI Fire & Smoke Detection
AI PPE Compliance Monitoring
AI Face Recognition & Access Control
AI LPR/ANPR
AI Slip & Fall Detection
AI Gun & Weapon Detection
AI Object Detection & Tracking
AI Animal Detection
AI Vehicle Identification & Tracking
AI Attendance Monitoring System
AI Access Control Systems
Automate employee attendance with AI-powered facial recognition — detect faces in real time, recognize registered employees, log timestamps automatically, and flag unauthorized individuals, using your existing CCTV or IP-camera video feed.
Traditional attendance methods often rely on manual registers, cards, or biometric devices that create long waiting queues. Our AI Face Recognition Attendance System automates the process using existing video infrastructure — it captures live video, detects faces, compares them against registered employee images, and automatically records attendance the moment a recognized employee is identified.
Designed around AI video analytics and computer vision, the system works with IP-camera video streams and provides a foundation for deployment across offices, factories, warehouses, and smart buildings.
The system analyzes live video and detects faces from the camera feed using computer vision technology.
Detected faces are compared with registered employee images to identify authorized personnel.
When an employee is recognized, the system records the employee's name, date and time automatically.
Recognized employees are shown with a green bounding box, while unknown individuals are flagged with a red "Unauthorized" label.
The attendance engine prevents multiple attendance entries for the same employee on the same day.
The solution is designed around live video input and can use existing IP-camera/RTSP video sources as the system develops toward full deployment.
The current MVP stores attendance records in CSV format with employee name, date and time, accurately logged.
Our facial recognition attendance technology combines InsightFace and OpenCV to detect and recognize registered employees from live video. Employee-specific recognition is based on separately maintained enrollment images, with approximately 3–5 photos per registered employee — a foundation for organizations moving from manual attendance toward automated, camera-based workforce monitoring.
A major advantage of an AI CCTV attendance solution is connecting attendance automation with existing video monitoring infrastructure. The documented system supports live video capture through OpenCV and identifies registered employees from the feed, with RTSP CCTV integration planned as the next development stage toward full IP-camera deployment.
Automated, real-time, camera-based attendance built to grow with your infrastructure.
An AI face recognition attendance system uses facial recognition technology to identify registered employees from camera feeds and automatically record their attendance with date and time.
The current documented MVP is webcam-based. RTSP/IP-camera integration is part of the planned roadmap, allowing the solution to progress toward full CCTV deployment.
The system detects a face from the video feed and compares it with images stored for registered employees. The project documentation specifies 3–5 enrollment photos per employee.
Yes. The current system is designed to mark each employee only once per day.
Yes. The current implementation visually distinguishes recognized employees from unknown individuals using different bounding-box indicators.
Multi-camera support is not implemented in the current MVP. Multi-camera tracking is included in the future roadmap.
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