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Official Implementation of the Paper "Enhancing 3D-Air Signature by Pen Tip Tail Trajectory Awareness: Dataset and Featuring by Novel Spatio-temporal CNN" Published in the IEEE International Joint Conference on Biometrics (IJCB) 2023

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3D-Signatures: T3AAS-v1 Verification Benchmarks

Paper arXiv License

Overview

Official PyTorch implementation of the IJCB 2023 paper:

"Enhancing 3D-Air Signature by Pen Tip Tail Trajectory Awareness: Dataset and Featuring by Novel Spatio-temporal CNN"

This repository contains benchmark implementations for air signature verification on the T3AAS-v1 dataset.

T3AAS-v1 Dataset

The T3AAS-v1 (Tip Tail Trajectory Aware Air Signature) dataset represents a significant advancement in 3D air signature verification research.

Requesting Access

To obtain access to the T3AAS-v1 dataset, please complete the data access request form:

Installation

Prerequisites

  • Python 3.7+
  • CUDA compatible GPU (recommended)

Setting up the Environment

Choose one of the following methods to install dependencies:

Using Conda (Recommended)

conda env create -f environment.yml
conda activate t3aas-env

Using Pip

pip install -r requirements.txt

Usage

Running Experiments

All experiments are managed through the main script:

python main.py [arguments]

To view all available arguments and options:

python main.py --help

Example Script

For convenience, we provide a sample execution script run.sh.

Citation

If you find this implementation useful for your research, please consider citing:

@INPROCEEDINGS{atreya2023enhancing,
  author={Atreya, Saurabh and Bora, Maheswar and Mukherjee, Aritra and Das, Abhijit},
  booktitle={2023 IEEE International Joint Conference on Biometrics (IJCB)}, 
  title={Enhancing 3D-Air Signature by Pen Tip Tail Trajectory Awareness: Dataset and Featuring by Novel Spatio-temporal CNN}, 
  year={2023},
  volume={},
  number={},
  pages={1-9},
  keywords={Three-dimensional displays;Biometrics (access control);Tail;Cameras;Forgery;Trajectory;Convolutional neural networks},
  doi={10.1109/IJCB57857.2023.10448666}
}

About Us

This research is conducted by the Machine Intelligence Group at BITS Pilani, Hyderabad Campus.

License

This project is licensed under the MIT License - see the LICENSE file for details.


© 2023 Machine Intelligence Group, BITS Pilani Hyderabad Campus. All Rights Reserved.

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Official Implementation of the Paper "Enhancing 3D-Air Signature by Pen Tip Tail Trajectory Awareness: Dataset and Featuring by Novel Spatio-temporal CNN" Published in the IEEE International Joint Conference on Biometrics (IJCB) 2023

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