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Source Code for IEEE BTAS 2019 paper: Palmprint Recognition Using Realistic Animation Aided Data Augmentation.

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Palm_NonLinear

Source Code for IEEE BTAS 2019 paper: Palmprint Recognition Using Realistic Animation Aided Data Augmentation.

Dependencies:

  1. Pytorch
  2. TensorboardX
  3. Numpy
  4. Scipy
  5. PIL
  6. OpenCV

The CPD code in the project is from https://github.com/siavashk/pycpd

Steps to train:

  1. Set the data path, the anim_frames path for the dataset in config file.
  2. Run cpd_transform.py script from the root folder of the project to generate the augmented dataset.
  3. Change the TRAIN_IMG_DIR in config file to cpd_data/CASIA/flip/ROI/train/ if training with CASIA augmented data for example.
  4. Change the network to desired model: example inceptionv3 or alexnet
  5. Change the OUTPUT_PATH appropriately to save the model being trained.
  6. Run main.py script from the root folder to train the model.

Config settings:

  1. Set STANDARD_AUGMENT to True and TRANSLATION_ONLY to False for full affine data augmentation.
  2. Set TRANSLATION_ONLY to True when using non-linearly augmented training data to combine them (NltTrans).
  3. Please refer to the paper to set other parameters.

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Source Code for IEEE BTAS 2019 paper: Palmprint Recognition Using Realistic Animation Aided Data Augmentation.

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