This is my final year project .

Project Overview

This project is a Face Recognition and Attendance System that uses dlib and OpenCV for face detection and recognition. It includes a Graphical User Interface (GUI) for ease of use, allowing users to:

• Capturing face images for students.
• Generating face embeddings for recognition.
• Training a face recognition model using the LBPH (Local Binary Patterns Histograms) algorithm.

• Marking attendance in a database based on recognized faces.

Features

• Face Detection: Detects faces using dlib's frontal face detector.
• Face Embeddings: Generates 128-dimensional embeddings for each face using dlib's face recognition model.
• Model Training: Trains the model for face recognition.
• Attendance Management: Marks attendance in a database and updates student points based on check-in time.
• Database Integration: Uses a MySQL database to store student details, attendance records, and points.

• Graphical User Interface: Provides an easy-to-use GUI for all functionalities.

Prerequisites

• Python: Version 3.7 or higher.
• Libraries:
  • dlib
  • opencv-python
  • numpy
  • pickle
  • tkinter (for GUI)
  • mysql-connector-python
• Database: MySQL server with the following schema:

  bash:
    CREATE TABLE IF NOT EXISTS students (
    id INT AUTO_INCREMENT PRIMARY KEY,
    roll_number VARCHAR(20) NOT NULL UNIQUE,
    name VARCHAR(100) NOT NULL,
    department VARCHAR(50),
    course VARCHAR(50),
    semester VARCHAR(20),
    points INT DEFAULT 0
     );
  
    CREATE TABLE IF NOT EXISTS student_images (
    id INT AUTO_INCREMENT PRIMARY KEY,
    student_id INT NOT NULL,
    image_path VARCHAR(255),
    FOREIGN KEY (student_id) REFERENCES students(id) ON DELETE CASCADE
    );
  
    CREATE TABLE IF NOT EXISTS attendance (
    id INT AUTO_INCREMENT PRIMARY KEY,
    student_id INT NOT NULL,
    date DATE NOT NULL,
    time TIME NOT NULL,
    FOREIGN KEY (student_id) REFERENCES students(id) ON DELETE CASCADE
    );
  

  ---

  Project Structure

env test/
│
├── capture_faces.py       # Script to capture face images for students
├── train_model.py         # Script to generate embeddings and train the model
├── recognize_face.py      # Script to recognize faces and mark attendance
├── gui.py                 # GUI for interacting with the system
├── db_connection.py       # Database connection utility
├── embeddings.pkl         # File to store face embeddings and labels
├── trained_model.yml      # Trained LBPH model for face recognition
├── dataset/               # Directory to store student face images
│   ├── <roll_number>/     # Subdirectory for each student
│   │   ├── image1.jpg
│   │   ├── image2.jpg
│   │   └── ...

---

How to Use

1. Setup the Environment
   • Install the required Python libraries:

   •   bash:
       pip install dlib opencv-python numpy mysql-connector-python
     

   • Set up the MySQL database using the schema provided above.
2. Run the GUI
   • Launch the GUI by running the gui.py script:

   •   bash:
            python gui.py
     

   • Use the GUI to:
     • Capture face images for students.
     • Train the face recognition model.
     • Recognize faces and mark attendance.
3. Manual Execution (Optional)
   • If you prefer to run the scripts manually:

     • Capture Face Images

       • Run the capture_faces.py script to capture face images for a student:

       • bash:  
         python capture_faces.py
         

       • Follow the on-screen instructions to capture 150 images for each student.

     • Generate Embeddings

       • Run the train_model.py script to generate embeddings for the captured images:

       • bash:
         python train_model.py
         

       • This will create or update the embeddings.pkl file.

     • Train the Model

       • The train_model.py script also trains the LBPH face recognizer and saves the model as trained_model.yml.

     • Recognize Faces and Mark Attendance

       • Run the recognize_face.py script to start face recognition and mark attendance:

       •  bash:
            python recognize_face.py
         

       • The script will:
         • Recognize faces in real-time using the webcam.
         • Mark attendance in the database.
         • Award points based on check-in time.

GUI Features

• The GUI provides the following functionalities:
  1. Capture Images:
     • Allows users to capture face images for a student by entering the roll number.
  2. Train Model:
     • Trains the face recognition model using the captured images.
  3. Recognize Faces:
     • Starts the face recognition process and marks attendance in the database.

Notes

• Ensure that the shape_predictor_68_face_landmarks.dat and dlib_face_recognition_resnet_model_v1.dat files are in the project directory. These files are required for dlib's face detection and recognition.
• The embeddings.pkl file stores face embeddings and labels. Do not delete this file unless you want to regenerate embeddings.

Future Improvements

• Add email notifications for attendance.
• Implement real-time analytics for attendance tracking.
• Optimize the face recognition process for faster performance.