DeepFingerprint-Refiner

A Gated PartialConv + Self-Attention Approach to Fingerprint Restoration and Denoising

This repository contains a double-stage Generator (using Gated Partial Convolutions and Self-Attention) and a Patch Discriminator, designed to inpaint and refine fingerprint images. The model removes noise, bridges broken lines, and thins the ridges for a cleaner fingerprint restoration, producing visually and structurally consistent output.

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Table of Contents

1. Features
2. Model Architecture
3. Getting Started
   • Installation
   • Dataset Structure
   • Running the Training Script
4. Results
5. Project Structure
6. Usage & Examples
7. Contributing
8. License
9. Acknowledgments

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Features

• Double-Stage Gated PartialConv Generator to handle missing/noisy fingerprint regions.
• Self-Attention to capture global context and improve coherence in fingerprints.
• Patch Discriminator (LSGAN) for adversarial training on local patches.
• Post-processing with morphological operations and endpoint-bridging to restore continuity in ridges.
• Mixed Precision Training for faster runs and lower memory usage on GPUs.

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Model Architecture

Generator

1. Stage 1: Gated PartialConv U-Net (4-level depth)
2. Stage 2: Another Gated PartialConv U-Net refining the output of Stage 1
3. Self-Attention inserted in the bottleneck of each U-Net for global coherence.

Discriminator

• Patch Discriminator that operates on concatenated (noisy_input, generated/real_output) pairs, following the LSGAN style.

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Getting Started

Installation

1. Clone this repository:

   git clone https://github.com/YourUsername/DeepFingerprint-Refiner.git
   cd DeepFingerprint-Refiner

2. Install Dependencies (create a virtual environment if you prefer):

   pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118
   pip install numpy matplotlib opencv-python tqdm Pillow

   Adjust the PyTorch version and CUDA version above (cu118) to match your system configuration.

3. (Optional) Check GPU:

   import torch
   print(torch.cuda.is_available())  # Should print True if GPU is accessible

Dataset Structure

You need two directories:

• Clean dataset (CLEAN_DATASET_PATH): Ground-truth, noiseless fingerprint images.
• Noisy dataset (NOISY_DATASET_PATH): Noisy or partially corrupted fingerprint images.

Each directory should contain images in a consistent format (e.g., .tif, .png, or .jpg).
Ensure that file naming conventions allow the script to pair noisy and clean images properly.

Example folder structure:

processed_dataset_final/
├── clean_dataset/
│   ├── 0001_clean.tif
│   ├── 0002_clean.tif
│   └── ...
└── mixed_noisy_dataset/
    ├── 0001_noisy_001.tif
    ├── 0002_noisy_002.tif
    └── ...

The pairing logic in FingerprintDataset looks for filenames sharing certain segments.

Running the Training Script

1. Configure paths and hyperparameters in Train.py:

   # Paths & hyperparams in Train.py
   CLEAN_DATASET_PATH = r"D:\Deep learning\PROJECT\processed_dataset_final\clean_dataset"
   NOISY_DATASET_PATH = r"D:\Deep learning\PROJECT\processed_dataset_final\mixed_noisy_dataset"
   OUTPUT_DIR         = r"D:\Deep learning\PROJECT\FINAL-PROJECT\PROJECT-NEW-LATEST\FINAL"
   
   num_epochs         = 300
   batch_size         = 4
   learning_rate      = 2e-4
   lambda_L1          = 100

2. Run Training:

   python Train.py

3. Check Logs & Samples:
   • Training progression is displayed in the console (loss metrics).
   • Sample images are saved every 10 epochs in OUTPUT_DIR/samples/.

When training completes, the final model weights (generator_final.pth and discriminator_final.pth) will be saved in OUTPUT_DIR.

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Results

Qualitative Examples (from the saved samples/epoch_X.png):

1. Noisy Input: Original corrupted fingerprint.
2. Lines Drawn On Top: Generator output (Stage 2) with morphological bridging and endpoint connections.
3. Ground Truth: Clean dataset reference.

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Project Structure

A typical layout of the repository might look like this:

DeepFingerprint-Refiner/
├── samples/                # Generated sample images during training
├── training_results/       # Additional logs/results
├── Validation/
│   ├── epoch_progression/  # Checkpoints / partial results over epochs
│   ├── Test_Results/
│   ├── eval.py             # Evaluation script
│   ├── new_final.py        # Possibly a refined version of test or evaluation
│   ├── test-data-new.py    # Data loader or test script
│   ├── testing.py          # Official testing script
│   ├── batch_losses.png    # Example of recorded losses
│   ├── loss_curves.png     # Visualization of generator/discriminator losses
│   └── ...
├── discriminator_epoch_270.pth  # Example of saved discriminator checkpoint
├── generator_epoch_270.pth      # Example of saved generator checkpoint
├── Train.py                # Main training script
├── Data.py                # Data script
├── README.md               # Project documentation (this file)
└── ...

Feel free to adjust or reorganize to fit your workflow.

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Usage & Examples

1. Inference / Testing:

   • Modify testing.py (or your chosen script) to load the saved weights:

     generator = AdvancedDoubleStageGenerator()
     generator.load_state_dict(torch.load("path/to/generator_final.pth"))
     generator.eval()
     ...
     # Then run inference on new images

2. Extend Post-Processing:

   • You can further tweak morphological operations (e.g., kernel sizes, thinning parameters) in the code around:

     morphological_bridging()
     thin_lines()
     connect_endpoints()

3. Visualizing:

   • Use matplotlib, OpenCV, or any library of your choice to monitor the generated fingerprint images.

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Contributing

Contributions, suggestions, and fixes are welcome!

1. Fork the repository
2. Create a new feature branch
3. Commit your changes
4. Open a Pull Request

We appreciate all improvements, ranging from bug fixes to new functionalities.

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License

This project is licensed under the terms of the MIT License. You are free to use, modify, and distribute this software, with attribution to this repository.

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Acknowledgments

• PyTorch for an elegant deep learning framework.
• NVIDIA PartialConv Paper for the original PartialConvolution approach:
  [“Image Inpainting for Irregular Holes Using Partial Convolutions” – Liu et al., ECCV 2018]
• Self-Attention references:
  [“Self-Attention Generative Adversarial Networks” – Zhang et al., ICML 2019]

Special thanks to all open-source contributors and the research community working on image inpainting, GANs, and fingerprint enhancement.

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Disclaimer: This repository is intended for research and educational purposes. Performance on different datasets may vary. Always ensure compliance with applicable laws and regulations when working with fingerprint or biometric data.