AI-Powered Attendance System Using Face Recognition
This project aims to build an intelligent attendance system that identifies individuals using face recognition. The goal is to replace manual attendance, reduce errors, and ensure fast and accurate tracking in organizations such as schools, universities, or companies.
The system detects and recognizes faces through a webcam or IP camera, then records attendance data (name, date, time, and status) automatically in a database or Excel file.
1. System Workflow
Face Detection: The camera captures live video, and the system detects faces using AI models like MTCNN, Haar Cascade, or YOLO. Detected faces are cropped and preprocessed.
Face Recognition: Each face is converted into a numeric embedding using models such as FaceNet, VGGFace, or DeepFace to represent unique facial features.
Matching & Verification: The embedding is compared with stored data. If similarity exceeds the threshold, the person is recognized; otherwise marked “Unknown.”
Attendance Logging: Identified users’ details (name, time, status) are saved securely in CSV, Excel, or database formats. Reports can be exported or linked to HR systems.
2. Key Modules
Camera Module: Captures real-time video.
Detection Module: Locates faces in frames.
Recognition Module: Compares embeddings to database.
Database Module: Stores users and logs.
Dashboard/UI: Displays reports and analytics.
3. Tools & Technologies
Language: Python
Libraries: OpenCV, NumPy, Pandas, DeepFace
Models: FaceNet, VGGFace, Dlib
Database: SQLite / MySQL
Frontend (optional): Flask + HTML/CSS
Hardware: Webcam or CCTV
4. Reports & Output
Generates daily or monthly attendance reports including check-in/out times, working hours, and punctuality. Notifications can be sent for absences or delays.
5. Benefits
Automation: No manual input needed.
Accuracy: Reliable AI-based recognition.
Security: Prevents proxy attendance.
Efficiency: Saves administrative time.
Scalability: Easy integration with HR systems.
6. Limitations
Accuracy depends on lighting and camera quality.
Recognition may fail if faces are covered or heavily changed.
Requires initial face dataset for registration.
7. Conclusion
The AI-Powered Attendance System provides a modern, contactless solution for attendance tracking. It combines face detection, recognition, and automatic logging to deliver an accurate, efficient, and secure system ideal for educational, business, and corporate environments.
