Objective

To develop an automated attendance tracking system using face recognition technology that records student presence in real-time, improves accuracy, reduces manual work, and integrates seamlessly with educational platforms.

Key Features

Student Panel:

• Secure registration and login

• Profile creation with facial data for recognition

• View personal attendance history

• Receive notifications for absences or low attendance

Teacher Panel:

• View live attendance during lectures

• Monitor class-wise attendance records

• Generate attendance reports and analytics

• Receive alerts for students with low attendance

Admin Panel:

• Manage users (students, teachers, classes)

• Approve or remove registered students/teachers

• Oversee facial recognition model performance

• Generate detailed reports on attendance trends

• Export attendance records for compliance or record-keeping

Tech Stack

Workflow (Step-by-Step)

1. User Registration & Facial Enrollment

• Students and teachers register on the platform

• Capture multiple facial images for model training

• Facial embeddings stored securely in the database

2. Lecture/Session Attendance

• Teacher starts a class session on the platform

• Students present in class are captured via webcam

• Face recognition algorithm matches captured images with stored embeddings

• Attendance is marked automatically in real-time

3. Attendance Monitoring

• Teachers can view live attendance dashboard

• Students absent or unrecognized are flagged

• Optional manual override if recognition fails

4. Reporting & Analytics

• Generate class-wise attendance summaries

• Track individual student attendance over time

• Identify trends such as frequent absentees

• Export reports as CSV, PDF, or integrate with LMS

5. Notifications

• Automated alerts for low attendance or missed sessions

• Email/SMS reminders for upcoming classes

6. Optional Features

• Integration with mobile devices for remote attendance

• Multi-class recognition for simultaneous sessions

• AI-based anomaly detection for fraudulent attendance