AI-Based Smart Attendance System Using Real-Time Facial Recognition
Automated contactless attendance tracking system with anti-spoofing liveness detection and automated Excel reporting.
Problem Statement
Manual paper-based attendance and biometric fingerprint scanners are time-consuming, prone to proxy attendance, and unhygienic in high-traffic campus environments.
Proposed Solution
Develops an automated, contactless multi-camera facial recognition pipeline powered by FaceNet embeddings with 99.2% accuracy. Incorporates blink detection to block photo/screen spoofing.
Key System Features
System Architecture & Pipeline
End-to-end data transformation pipeline from input capture to output visualization.
Project Modules Breakdown
Video Ingestion & Face Detection
Module 1Captures live RTSP/webcam video feeds, extracts individual faces using Haar Cascades and MTCNN.
Liveness & Anti-Spoofing Unit
Module 2Detects eye blinks using Eye Aspect Ratio (EAR) and skin reflection to prevent photo presentation attacks.
FaceNet Matcher & Database Module
Module 3Computes Euclidean distance between captured embeddings and enrolled student vector embeddings.
Faculty & Admin Portal
Module 4Web interface for student registration, timetable allocation, and export of audit-ready attendance sheets.
Complete Technology Stack
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