Trinetra Smart Attendance System

Smart attendance systems are essential for modern academic management and security. Traditional attendance systems rely on manual registers or basic QR codes. These methods often overlook environmental factors and device integrity, making them easy targets for proxy attendance and spoofing. In a campus setting, accurate identity verification depends not just on facial features but also on location and device security. This project introduces Trinetra, a smart attendance system that considers context. It uses a dual-stream verification model that combines an Ocular Feature Extraction algorithm for precise biometric authentication with a Geofencing Protocol for location-based verification. The biometric stream focuses on the eye area to reduce masking and background interference. It uses Brightness Normalization and Cosine Similarity to confirm identities. At the same time, the system conducts an integrity check to spot and prevent "Developer Options," which helps block GPS spoofing and virtual camera attacks. The system functions as a real-time Android app connected to a Firebase Realtime Database. This setup allows for secure attendance marking through front-camera scanning, up-to-date location tracking, and automatic reporting. Test results show that this approach is much more resistant to fraud and performs significantly better in different lighting conditions compared to traditional facial recognition or single-modal biometric systems.