Fingerprint Matching using SIFT (Scale-Invariant Feature Transform)

This project demonstrates fingerprint matching using the SIFT (Scale-Invariant Feature Transform) algorithm and FLANN-based feature matching in OpenCV. It compares a given fingerprint image (sample) with a dataset of real fingerprints to find the best match based on feature similarity.

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📘 Overview

The goal of this project is to identify the most similar fingerprint image from a dataset when compared with a given altered fingerprint image. This can be useful for applications such as biometric verification, forensics, and image-based identification systems.

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⚙️ How It Works

1. Load a sample fingerprint (e.g., an altered image).

2. Iterate through real fingerprint images in the dataset.

3. For each comparison:

   • Detect keypoints and compute descriptors using SIFT.
   • Match features using the FLANN-based matcher.
   • Apply a ratio test (Lowe’s test) to filter good matches.

4. Compute a similarity score based on the ratio of good matches to total keypoints.

5. Display the best matching fingerprint and the match visualization.

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🧠 Technologies Used

• Python 3
• OpenCV (cv2)
• SIFT Algorithm
• FLANN-based Matcher

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📂 Dataset

I am using the SOCOFing (Sokoto Coventry Fingerprint Dataset) downloaded from Kaggle. Due to its large size, it cannot be uploaded to GitHub. You can download it using the link below:

🔗 SOCOFing Dataset on Kaggle

Dataset Structure (simplified):

SOCOFing/
│
├── Real/
│   ├── 1__M_Left_index_finger.BMP
│   ├── 2__M_Right_thumb_finger.BMP
│   └── ...
│
└── Altered/
    ├── Altered-Easy/
    ├── Altered-Medium/
    └── Altered-Hard/

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🚀 Usage

1. Clone the repository

   git clone https://github.com/your-username/fingerprint-matching.git
   cd fingerprint-matching

2. Install dependencies

   pip install opencv-python opencv-contrib-python

3. Place the dataset

   • Download the dataset from Kaggle.
   • Extract it into the project directory, maintaining the folder structure shown above.

4. Run the script

   python fingerprint_match.py

5. View the output

   • The console will display progress and the best matching filename.
   • A window will show the side-by-side match visualization.

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🖼️ Sample Image

Sample (Input Fingerprint):

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🧩 Output Image

Best Match Visualization:

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📊 Future Improvements

• Support for batch comparison and result export.
• Integration with deep learning-based feature extraction.
• Improve accuracy and speed using parallel processing.

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🧑‍💻 Author

Developed by Prtham Feel free to reach out for collaboration or suggestions!