An AI-powered web-based attendance management system featuring facial recognition for automated attendance tracking. The system supports three user roles: Admin, Teacher, and Student, each with role-specific dashboards and functionalities.

• Face Enrollment: Register student faces through admin panel.
• Live Self-Attendance: Students mark attendance using webcam with blink detection for liveness verification.
• Classroom Attendance: Teachers upload classroom photos for automatic batch attendance marking with detection for unknown faces.
• Attendance Protection: 'Present' status is protected for the calendar day and cannot be accidentally overwritten as 'Absent'.
• Mock Mode Support: System can run without AI libraries for testing (returns dummy data).

• Digital Submission: Students upload medical certificates or other documents directly via dashboard.
• OCR & AI Analysis: Automatic text extraction from uploaded images/PDFs using Tesseract OCR.
• Auto-Categorization: AI suggests category (Medical, Family, Personal) based on content.
• Teacher Workflow: Review interface with Approve, Reject, and Hold options.

• User management (add/remove teachers and students).
• Face enrollment system for students.
• Declare holidays and send emergency alerts.
• View comprehensive attendance reports.
• Automated email notifications to parents.
• Audit logging for all system actions.

• Modernized Dashboard: Clean, card-based UI with quick actions.
• Mark attendance manually or via Excel upload.
• Classroom photo attendance (AI-powered batch recognition) with feedback for unrecognized students.
• Automatic attendance percentage calculation with visual progress bars.
• Warning emails for students below 75% attendance.
• View student attendance analytics.
• Excuse Management: Review, Approve, Reject, or Hold student excuse letters with AI insights.

• Live self-attendance with webcam (face + blink verification).
• View personal attendance records and statistics.
• Receive email notifications for low attendance.
• Access to holiday and alert announcements.
• Excuse Submission: Easy-to-use form for submitting absence excuses with medical document upload.

• Java 8 - Core application logic.
• Servlet/JSP - Web application framework.
• PostgreSQL JDBC - Direct database connectivity for performance.
• Maven - Dependency management and build tool.
• Apache Tomcat 9 - Application server.

• HTML5/CSS3 - Structure and styling.
• JavaScript - Client-side interactivity.
• Bootstrap 5 - Responsive UI framework.
• FontAwesome 6 - Vector icons.
• SweetAlert2 - Modern popup notifications.
• AJAX - Asynchronous requests.

• Python 3.13 - AI service runtime.
• Flask - Python web framework.
• OpenCV - Image processing.
• face_recognition - Face detection and encoding (based on dlib).
• NumPy - Numerical computations.
• Tesseract OCR - Optical Character Recognition for document parsing.
• Pillow (PIL) - Image processing helper.

• PostgreSQL - Primary database (attendance_system).
• Schema includes: users, students, teachers, attendance records, face data, audit logs.

• BCrypt password hashing.
• Session-based authentication.
• Role-based access control (RBAC).

┌─────────────────┐         ┌──────────────────┐
│   Web Browser   │ ◄─────► │  Tomcat Server   │
│   (Frontend)    │         │  (Java Backend)  │
└─────────────────┘         └────────┬─────────┘
                                     │
                                     │ HTTP
                                     ▼
                            ┌─────────────────┐
                            │  Python AI      │
                            │  Service :5000  │
                            └─────────────────┘
                                     │
                                     ▼
                            ┌─────────────────┐
                            │   PostgreSQL    │
                            │    Database     │
                            └─────────────────┘

• Java 8 or higher
• Maven 3.6+
• PostgreSQL 12+
• Python 3.8+
• (Optional) cmake - Required for full AI functionality

git clone https://github.com/samiralam04/StudentAttendanceManagementSystem.git
cd StudentAttendanceManagementSystem

# Create PostgreSQL database
createdb attendance_system

# Configuration:
# Create a .env file in the root directory with:
DB_URL=jdbc:postgresql://localhost:5432/attendance_system
DB_USER=your_username
DB_PASSWORD=your_password

cd python_services

# Create virtual environment
python3 -m venv venv

# Activate virtual environment
source venv/bin/activate  # macOS/Linux
# OR
venv\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

# For full AI functionality (optional):
brew install cmake  # macOS
# OR
sudo apt-get install cmake  # Ubuntu/Debian

# Then install face_recognition
pip install face_recognition

# Install OCR dependencies
pip install pytesseract Pillow

# From python_services directory
./start_python_service.sh

# Verify with: curl http://localhost:5000/status

# From project root directory
mvn clean package cargo:run

# Application will start on http://localhost:8080/StudentAttendanceManagementSystem

1. Python AI Service (Terminal 1):

   cd python_services
   ./start_python_service.sh

2. Java Application (Terminal 2):

   mvn cargo:run

Problem: "Python service unreachable"

• Ensure Python service is running: ps aux | grep app.py
• Check port 5000 is available: lsof -i :5000
• Restart service: pkill -f app.py && ./start_python_service.sh

Problem: "AI libraries not available"

• This is expected if cmake/face_recognition isn't installed. System runs in mock mode (returns dummy data).

Problem: "Port 8080 already in use"

• Kill existing process: lsof -ti:8080 | xargs kill -9
• Restart: mvn cargo:run

Problem: "Database connection failed"

• Verify PostgreSQL is running.
• Check credentials in .env file.

The system implements strict attendance rules:

• Daily Lock: Once a student is marked "Present", they cannot be marked "Absent" for the rest of the day by automatic systems (prevents overwriting by classroom photos where they might be out of frame).
• Manual Overrides: Teachers can still upgrade an "Absent" status to "Present" if needed.
• Reporting: Automatic calculation of percentage with color-coded status indicators (Green > 75%, Yellow > 50%, Red < 50%).

The classroom attendance feature processes group photos. If any student is detected but not recognized (not matching any enrolled encoding), the system provides a warning message to the teacher: "One or more students were not recognized."

This project is licensed under the MIT License. © 2026 Samir Alam. All rights reserved.

Developer: Samir Alam
Year: 2026

Feel free to reach out for suggestions, collaborations, or support.