Automated Attendance System using Face Recognition (Python)

This project implements an automated classroom attendance system using Python and OpenCV: student faces are first registered by capturing a few reference images per student, from which face encodings are computed using the face_recognition library (built on dlib).

During a class session, a webcam feed is scanned for faces, each detected face is compared against the stored encodings, and matched students are automatically marked present in a CSV/SQLite attendance log with a timestamp, avoiding manual roll-call.

The system displays a live video feed with bounding boxes and names overlaid on recognized students, handles multiple faces per frame, and includes a simple report generator that exports monthly attendance percentages per student to an Excel file using openpyxl.

Detailed Project Description

Automated Attendance System using Face Recognition (Python) replaces manual registers with automatic, tamper-resistant attendance marking, storing every entry with a timestamp so reports can be generated instantly.

How It Works
1. Each user is enrolled once (card / fingerprint / face / login) and stored with an ID.
2. On arrival the system identifies the user, checks against the enrolled records and marks attendance with date and time.
3. Records are saved to a database and shown on a dashboard or exported as reports.
4. Duplicate or late entries are handled according to the configured rules.

Technology Roles
- Python
- OpenCV
- face_recognition
- dlib
- openpyxl

Implementation Steps
1. Define requirements and draw the ER / class diagram and screen flow.
2. Set up the environment (Python, OpenCV, face_recognition (dlib)) and create the project skeleton.
3. Design the database schema / data model and seed it with sample data.
4. Implement the core modules with validation and role-based access where needed.
5. Build the user interface and connect it to the backend / data layer.
6. Test each module (unit + manual), fix edge cases and secure inputs (SQL injection, XSS, auth).
7. Prepare documentation, screenshots and a demo build.

Key Features
- One-time enrolment
- Instant identification
- Timestamped records
- Daily / monthly reports
- Admin panel

Expected Output
On completion the student will have an attendance log with name, ID, date and time for every entry, plus summary reports.

Deliverables
- Complete source code with comments
- Database schema / data model and screen flow
- Project report with screenshots and results
- Demonstration-ready build