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Finger Vein Biometrics Classifier

Identify the Person using Finger Vein Pattern, trained with CNN Classifier (Trained Weights Included)
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Table of Contents

About The Project

Originally started as a freelance project, this is a simple classifier designed to identify person based on their finger vein images.

Dataset Information

Results

Basic Training Results/Curves are shown below.

Accuracy Curves

Accuracy

Loss Curves

Loss

Confusion Matrix

Confusion Matrix

How to Run

The experiment should be fairly reproducible. However, a GPU would be recommended for training. For Inference, a CPU System would suffice.

Hardware Used for the Experiment

  • CPU: AMD Ryzen 7 3700X - 8 Cores 16 Threads
  • GPU: Nvidia GeForce RTX 2080 Ti 11 GB
  • RAM: 32 GB DDR4 @ 3200 MHz
  • Storage: 1 TB NVMe SSD (This is not important, even a normal SSD would suffice)
  • OS: Ubuntu 20.10

Alternative Option: Google Colaboratory - GPU Kernel

Built With

Simple List of Deep Learning Libraries. The main Architecture/Model is developed with Keras, which has a dependency on Tensorflow 1.x

The exact library versions can be found in the requirements.txt file.

Changelog

Since this is a Freelancing Project, I am not maintaining a CHANGELOG.md.

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Animikh Aich