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An automated attendance management system using real-time face recognition with deep learning. Captures student faces via webcam or CCTV, marks attendance automatically, and generates reports for teachers and administrators.
Problem Statement
Traditional attendance systems using paper or biometrics are slow, prone to proxy attendance, and lack real-time reporting. Educational institutions need a contactless, fast, and reliable attendance solution.
Objectives
Implement real-time face detection and recognition using deep learning
Build automated attendance marking with anti-spoofing measures
Create dashboards for students, teachers, and administrators
Generate attendance reports and analytics
Support multiple classrooms and batch processing
Modules
Face Registration
Capture and store student face embeddings during enrollment with multiple angle shots.
Face Recognition Engine
Real-time face detection and recognition using OpenCV and deep learning models.
Attendance Manager
Automatic attendance logging with timestamp, location, and confidence score.
Admin Dashboard
Web-based dashboard for managing students, viewing reports, and configuring settings.
Notification System
Email/SMS alerts for low attendance and daily reports to parents.
Expected Output
A web application with camera integration that automatically detects and recognizes student faces, marks attendance in real-time, and provides comprehensive analytics dashboards.
Future Scope
Mask detection, emotion analysis, integration with LMS platforms, and mobile app for students to check their attendance records.