Smart Face Attendance System

The Smart Face Attendance System is a facial recognition-based solution for efficiently managing attendance records. This application uses machine learning algorithms to detect and recognize faces, ensuring accurate and automated attendance tracking.

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Features

• Face Detection: Uses pre-trained Haar Cascade classifiers for facial detection.
• Face Recognition: Implements FaceNet's InceptionResnetV1 model for high-accuracy face recognition using embeddings.
• Student Management: Manages student data through a dedicated database.
• Attendance Tracking: Records attendance, including late entries, in CSV files.
• Database Support: Attendance and student data are stored in an SQLite database for reliability and easy access.
• Text-to-Speech Feedback: Provides auditory feedback using Windows SAPI.

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Directory Structure

smart face attendance system/
├── haarcascade_frontalface_default.xml   # Pre-trained face detection model
├── main.py                              # Main script for running the application
├── student_db.py                        # Script for managing student database
├── Attendance/
│   ├── Attendance_.csv                  # Records of attendance
│   └── late_attendance_record.csv       # Records of late entries
├── data/
│   └── attendance.db                    # SQLite database for attendance data

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Setup and Installation

1. Clone the Repository:

   git clone <https://github.com/ragultv/smart-face-attendance-system.git>
   cd smart-face-attendance-system

2. Install Required Libraries: Make sure you have Python 3.x installed. Install dependencies using:

   pip install -r requirements.txt

3. Run the Application: Execute the main script to start the system:

   python main.py

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How It Works

1. Face Detection: The haarcascade_frontalface_default.xml file is used for detecting faces in real time.
2. Face Recognition: Face embeddings are generated using the FaceNet model (InceptionResnetV1), and matches are identified based on cosine similarity.
3. Database Management: The attendance.db file stores all attendance records securely.
4. Attendance Records: CSV files in the Attendance/ folder maintain detailed logs of attendance and late entries.
5. Text-to-Speech: Provides auditory feedback for enhanced user experience.

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Dependencies

• OpenCV
• SQLite3
• Pandas
• NumPy
• PyTorch
• Facenet-PyTorch
• pywin32

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Contribution

Feel free to fork this repository and submit pull requests to improve the system.

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