Facial Recognition-Based Attendance System: Computer Engineering Guide
1. Introduction
Overview of the project.
Objectives of the system.
Scope of the system.
2. Requirements Analysis
Functional Requirements:
· - User roles: Admin, Teacher, Student.
· - Key functionalities: User registration, Facial recognition, Attendance logging, Reporting.
Non-Functional Requirements:
· - Real-time processing, Accuracy, Scalability, Security.
3. System Design
Architecture:
· - Client-Server Architecture.
· - Use of AI models for facial recognition.
Data Flow Diagrams (DFDs):
· - Level 0: System overview.
· - Level 1: Modules like User Management, Attendance Tracking, and Reporting.
Database Design:
· - ER Diagram.
· - Tables: Users, Attendance, Facial Data, Reports.
4. Technology Stack
Frontend: React.js, Angular, or Flutter.
Backend: Python (Django/Flask), Node.js, or Java (Spring Boot).
Database: PostgreSQL, MySQL, or MongoDB.
Facial Recognition Library: OpenCV, dlib, or DeepFace.
Hosting: AWS, Azure, or Google Cloud.
5. Implementation
Frontend Development:
· - User-friendly interface for attendance management.
· - Camera integration for real-time facial capture.
Backend Development:
· - API development for storing and retrieving attendance data.
· - Integration of AI models for facial recognition.
Database Development:
· - Schema design to accommodate user and attendance data.
· - Use of NoSQL for facial data storage, if required.
6. Security
Secure authentication mechanisms (e.g., JWT or OAuth).
Facial data encryption using AES or similar.
Adherence to data privacy regulations (e.g., GDPR).
7. Testing
Unit Testing: Validate individual components.
Integration Testing: Ensure seamless module interactions.
System Testing: Test the application end-to-end.
AI Model Testing: Evaluate accuracy and robustness of facial recognition.
8. Deployment
CI/CD Pipeline setup using Jenkins, GitHub Actions, or GitLab CI.
Deployment strategies: Rolling updates or Blue-Green deployments.
Hosting on cloud services with real-time database integration.
9. Maintenance and Updates
Regular updates to AI models for better accuracy.
Tracking and fixing bugs using tools like JIRA.
Monitoring system performance with tools like Prometheus.
10. Appendix
Glossary of terms.
References and resources.