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Artificial Intelligence · Major Project
Face Recognition Attendance System
Camera-based attendance with liveness detection and analytics
2-3 weeks build
Overview
A classroom attendance system that recognises enrolled students from a live camera feed, blocks photo spoofing with a blink-based liveness check, and produces per-subject attendance analytics that staff can export.
What makes it stand up
- 01Embedding-based recognition that scales to hundreds of students
- 02Blink and head-motion liveness to defeat printed photos
- 03Automatic register marking with confidence thresholds
- 04Defaulter reports and subject-wise attendance charts
- 05Offline desktop mode plus a web dashboard
Module breakdown
- 01
Enrolment
Guided multi-angle capture and embedding storage.
- 02
Recognition
Detection, alignment, embedding and nearest-neighbour match.
- 03
Liveness
Eye-aspect-ratio blink detection and motion checks.
- 04
Records
Timetable-aware marking with a manual override trail.
- 05
Analytics
Trends, defaulter lists and PDF or CSV export.
After this, you will be able to
- Explain embeddings versus classification for faces
- Discuss your privacy and consent design choices
- Demo recognition live in front of examiners
Technology stack
- Python
- OpenCV
- FaceNet
- Flask
- SQLite
- React
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 2-3 weeks
- Difficulty
- Rating
- 4.8 / 5 · 301
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support