Neighbor-based Bag of Samplings for Person Identification Through Handwritten Dynamics and Convolutional Neural Networks

This repository holds all the necessary code to run the very-same experiments described in the paper "Neighbor-based Bag of Samplings for Person Identification Through Handwritten Dynamics and Convolutional Neural Networks".

References

If you use our work to fulfill any of your needs, please cite us:

@article{deRosa:22,
  author = {de Rosa, Gustavo H. and Roder, Mateus and Papa, João P.},
  title = {Neighbour-based bag-of-samplings for person identification through handwritten dynamics and convolutional neural networks},
  journal = {Expert Systems},
  volume = {39},
  number = {4},
  pages = {e12891},
  keywords = {bag-of-samplings, biometrics, convolutional neural networks, handwritten dynamics, person identification},
  doi = {https://doi.org/10.1111/exsy.12891},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.12891},
  eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.12891},
  year = {2022}
}

Structure

• data/: The data folder itself;
• datasets/
  • sampled.py: A Sampled class that helps a user dealing with the bag-of-samplings approach over the raw signals;
• models/
  • alexnet.py: An AlexNet architecture implementation;
  • cifar10.py: A CifarNet architecture implementation;
  • lenet.py: A LeNet architecture implementation;
• utils/
  • loader.py: Methods that assist in loading the raw signals;
  • sampler.py: Methods that assist in sampling the raw signals.

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Package Guidelines

Installation

Install all the pre-needed requirements using:

pip install -r requirements.txt

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Downloading the Data

One can download the person idenfication-based NewHandPD and SignRec datasets. After downloading the file, decompress it and move it to the data/ folder. Thus, their path should look like this: data/handpd/ and data/signrec/.

Each handpd/ folder will contain an integer identifier (person identifier) and twelve .txt signals, as follows:

• (1, 2): Circles;
• (3, 4): Diakinesis;
• (5, 6, 7, 8): Meanders;
• (9, 10, 11, 12): Spirals.

Each signrec/ folder will contain an integer identifier (person identifier) and ten .txt signals, as follows:

• (1, 2, 3, 4, 5, 6, 7, 8, 9, 10): Handwritten phrases;

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Usage

Training an Architecture

After gathering the data, now it is possible to train an architecture. There are currently two ways in order to fulfill that purpose:

• sampled_split.py: Samples the data and trains a network using a train/test split approach;
• sampled_kfolds.py: Samples the data and trains a network using k-fold cross-validation.

Calculating Metrics

Each one of the training scripts will output a .pkl file, which contains valuable information about the training process. One can run the following script to calculate some metrics from that process:

python calculate_metrics.py

Plotting Graphics

Finally, one can use one of the following scripts in order to generate a graphic regarding the architectures' comparison:

• plot_computational_load.py: Plots the computational load (training time) of the architectures;
• plot_loss_convergence.py: Plots the training loss convergence of the architectures.

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Support

We know that we do our best, but it is inevitable to acknowledge that we make mistakes. If you ever need to report a bug, report a problem, talk to us, please do so! We will be available at our bests at this repository.

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