Developed a face recognition-based attendance system with anti-spoofing, enabling secure, real-time attendance marking while preventing fraudulent access using photos or videos.
Built a secure face recognition attendance system that automates attendance marking using real-time computer vision, enhanced with anti-spoofing mechanisms to prevent fraudulent identification attempts. The system captures facial data, trains recognition models, and verifies users through live detection before marking attendance.
Implemented a complete pipeline including face detection, recognition, and liveness verification, ensuring that only genuine users (not images or recorded videos) are authenticated. Integrated webcam-based input with real-time processing to deliver fast and reliable identification.
Attendance data is automatically recorded with timestamps and stored in structured formats for tracking and analysis. The project demonstrates strong understanding of computer vision, security-focused ML systems, and real-time application design, making it suitable for deployment in educational institutions or workplaces.
Features
- Real-time face detection and recognition using webcam input
- Anti-spoofing (liveness detection) to prevent photo/video-based fraud
- Automated attendance marking with accurate timestamp logging
- Efficient computer vision processing using OpenCV and ML techniques
- End-to-end pipeline from data collection to real-time deployment