Identify the Person using Finger Vein Pattern, trained with CNN Classifier (Trained Weights Included)
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- Table of Contents
- About The Project
- Dataset Information
- Results
- How to Run
- Changelog
- Contributing
- License
- Contact
Originally started as a freelance project, this is a simple classifier designed to identify person based on their finger vein images.
- The original dataset used is: SDUMLA-HMT Database.
- Dataset URL: http://www.wavelab.at/sources/Prommegger19c/
Basic Training Results/Curves are shown below.
The experiment should be fairly reproducible. However, a GPU would be recommended for training. For Inference, a CPU System would suffice.
- 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
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.
Since this is a Freelancing Project, I am not maintaining a CHANGELOG.md.
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.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
- Website: Animikh Aich - Website
- LinkedIn: animikh-aich
- Email: animikhaich@gmail.com
- Twitter: @AichAnimikh