Research Abstract
This work explores facial recognition-enabled attendance capture to reduce manual overhead, improve record accuracy, and provide transparent timestamped logs for institutional monitoring.
Research Detail
AI Based Attendance System Using Face Recognition
Applied computer vision research on automated identity verification for attendance workflows in educational and organizational environments.
Applied Computer Vision Research
Status: Communicated
Focus: Automation & Reliability
Objective
Design a practical attendance framework that can identify registered individuals in real time, prevent duplicate entries, and generate trustworthy attendance data for administrators.
Domain
Education Technology
Current Status
Communicated
Core Engine
Face Recognition Pipeline
AI/ML Concepts Used
- Face detection and embedding-based recognition
- Threshold-based matching confidence control
- Preprocessing for lighting and frame variability
- Identity deduplication and event logging logic
- Performance-awareness for real-time inference loops
Implementation Idea
A camera stream ingestion module detects faces, maps embeddings against a registered database, and writes attendance records with timestamps. The proposed architecture supports dashboards, reports, and future cloud synchronization.
Skills & Insights Developed
- End-to-end design of AI-powered automation systems
- Balancing accuracy with execution speed in CV tasks
- Data integrity planning for operational systems
- Converting research concepts into deployable product pathways
Current Status
The research has been communicated and continues to evolve through implementation-focused improvements in robustness, user experience, and deployment readiness.