tpa-cnn

This repository contains PyTorch implementation of the following paper: Stoimchev, M., Ivanovskа, M., Štruc, V. Learning to Combine Local and Global Image Information for Contactless Palmprint Recognition (This is an initial version, the code will be updated)

The Two-Path Architecture (TPA)

Table of Contents

• The Two-Path Architecture (TPA)
  • Installation
  • Training
  • Testing
  • Citing Two-Path Architecture
  • Reference

Dependencies

• Python3
• PyTorch
• Torchvision
• Numpy
• Matplotlib
• Pillow / PIL
• imgaug
• scikit-learn

Installation

1. First clone the repository

   git clone https://github.com/Marjan1111/tpa-cnn.git
   

2. Create the virtual environment via conda

   conda create -n tpa python=3.9
   

3. Activate the virtual environment.

   conda activate tpa
   

4. Install the dependencies.

   pip install -r requirements.txt
   

Training

To list the arguments, run the following command:

python main.py -h

Example how to train on IITD dataset

python main.py \     
    --backbone Vgg \         
    --data IITD \  # switch to: CASIA if you want to train on CASIA dataset      
    --palm_train left \  # note: if you chose left forr train, chose right for palm_test, and vice versa.
    --palm_test right \ 
    --n_epochs 100 \  
    --num_trainable 10 \ 
    --metric_head arc_margin \ 
    --patches [75, 1, 0, 30] \ 
    --lr_centers 0.5 \ 
    --alpha 0.001 \ 
    --save_path saved_models \ 
    --model_type Vgg_16 \ 

Testing

To start the all-vs-all evaluation protocol, run the following command:

python test.py

How to create the file structure for IITD and CASIA datasets

datasets
├── IITD_ROI
│   ├── Segmented
│          ├── Left
|          |   └── 001_1.bmp
|          |   ...
|          |   └── 230_5.bmp
|          |
│          ├── Right
|              └── 001_1.bmp
|              ...
|              └── 230_5.bmp
|
├── CASIA_ROI
        └── 0001_m_l_01.jpg
        ...
        └── 0312_m_r_11.jpg