Face Recognition Attendance System

This project implements a face recognition-based attendance system using several machine learning models. The entire pipeline leverages SRCNN, DeblurGANv2, and LIME models for image processing, along with a custom-trained VGGFace model for facial recognition. Below is a breakdown of the files, usage, and instructions for setting up the project.

Project Structure

1. SRCNN.py

• Purpose: Defines the SRCNN model used for image super-resolution.
• Details: Loads the pre-trained weights to enhance image quality.

2. DeblurGANv2.py

• Purpose: Implements the DeblurGANv2 model to remove blur from images.
• Details: Loads the necessary pre-trained weights for deblurring.

3. layer_utils.py

• Purpose: Contains essential utility functions and building blocks.
• Details: Includes implementations for instance normalization and custom layer definitions required for the models.

4. LIME.py

• Purpose: Implements the LIME model for light enhancement to improve lighting conditions in images.

5. pipeline.py

• Purpose: Cascades the SRCNN, DeblurGANv2, and LIME models to form the complete image processing pipeline.

6. crop.py

• Purpose: Uses OpenCV’s Haar Cascade classifier to crop faces from images.
• Usage: Processes raw images from the Dataset folder, extracts faces, and saves them to the Headsets folder.

7. test.py

• Purpose: Script to test the functionality of the trained VGGFace model.

8. VGGface_VGG16.ipynb

• Purpose: Jupyter notebook used for training the VGGFace model and creating the dataset.
• Dataset Structure:
  • Place images in the format:

    Dataset/{student_name}/images  
    

  • The crop.py extracts faces from these images and stores them in:

    Headsets/{student_name}/images  
    

9. Attendance.py

• Purpose: Main script for recognizing faces using the trained VGGFace model and logging attendance.
• Details: Uses the webcam to capture real-time images and recognizes students based on the trained VGGFace model.

10. Class.json

• Purpose: Holds the details of each student in the class as a JSON object, which is referenced during attendance logging.

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Prerequisites

• Python Version: 3.11.9
• TensorFlow Version: 2.17
• Ensure the following folders exist and contain the appropriate files:
  • Dataset/{student_name}/images (Raw Images)
  • Headsets/{student_name}/images (Extracted Faces)

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Setup Instructions

1. Install Dependencies:
   Install the required Python libraries using:

   pip install tensorflow opencv-python-headless numpy matplotlib

2. Download Weights:
   Download the pre-trained weights for all models from the following link:
   Google Drive - Weights
   Place the downloaded weights in the same directory as the code files.

3. Train the VGGFace Model:
   Open VGGface_VGG16.ipynb and run the notebook to train the VGGFace model on the dataset.
   Ensure the dataset follows the structure described above.

4. Extract Faces for Training:
   Run the crop.py script to extract faces from the dataset:

   python crop.py

5. Run the Attendance System:
   Use Attendance.py to launch the attendance logging system:

   python Attendance.py

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Usage

1. Testing the VGGFace Model:
   Use test.py to verify the VGGFace model's performance:

   python test.py

2. Image Enhancement Pipeline:
   Run the pipeline.py to process an image through the SRCNN, DeblurGANv2, and LIME models:

   python pipeline.py

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Folder Structure

/project-root  
│  
├── Dataset/  
│   └── {student_name}/images/ (Raw student images)  
├── Headsets/  
│   └── {student_name}/images/ (Cropped faces)  
├── SRCNN.py  
├── DeblurGANv2.py  
├── layer_utils.py  
├── LIME.py  
├── pipeline.py  
├── crop.py  
├── test.py  
├── VGGface_VGG16.ipynb  
├── Attendance.py  
└── Class.json  

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Notes

• Ensure all the pre-trained weights are in the correct folder to avoid loading errors.
• Verify that your webcam is properly connected and recognized by the system before running Attendance.py.
• Adjust the model parameters if needed during training in VGGface_VGG16.ipynb.

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