This system automates the process of taking attendance using facial recognition technology. Instead of calling names or using ID cards, the system captures students’ (or employees’) faces through a webcam, identifies them using machine learning, and marks their attendance accordingly.

???? Key Features

???? Face Detection & Recognition: Detects faces from a live webcam feed and identifies them using a trained model.

???? Automated Attendance Logging: Marks attendance and saves records with timestamps.

???? Database Integration: Stores student/employee information and attendance logs.

???? Profile Management: Add, update, or delete user profiles along with facial data.

???? Reports Generation: View attendance summaries by date, individual, or class/department.

⚙️ How It Works

Face Registration:

Each user registers by capturing multiple images of their face.

These images are used to train a face recognition model (e.g., using LBPH, Eigenfaces, or Fisherfaces).

Attendance Detection:

The system uses a webcam to capture real-time video.

It detects and recognizes faces using OpenCV and compares them with the stored dataset.

If a match is found, the user is marked as "Present" with a timestamp.

Database Handling:

Java interacts with a database (MySQL, SQLite, etc.) to store user info and attendance records.

Each record includes the date, time, user ID, and attendance status.

Technologies Used

✅ Advantages

Eliminates proxy attendance and human error.

Fast and contactless attendance marking.

Real-time monitoring and report generation.

Scalable for schools, universities, offices, or events.

???? Possible Extensions

Mobile App version with Android + Java/Kotlin.

SMS/Email Notifications for absent users.

Temperature Detection (for health checks, e.g., in pandemic scenarios).

Admin Dashboard with data visualization using JavaFX charts.

Database Tables Example

users: id, name, photo, email

attendance: id, user_id, date, time, status

???? Tools You Can Use

OpenCV + JavaCV (for face detection & recognition)

JavaFX or Swing (for GUI)

MySQL or SQLite (for data storage)

Eclipse / IntelliJ (IDE for Java development)

⚠️ Challenges

• Lighting and camera quality can affect face recognition.
• Requires a well-trained dataset for accurate detection.
• Real-time processing may need optimization on low-end systems.