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

Saurabh Atreya, Maheswar Bora, Aritra Mukherjee, Abhijit Das
1/5/2024
cs.CV

No official code found. Best implementation: atrey-a/t3aas-v1-benchmarking (0 stars, last commit 2025-03-19). 1 more below.

Abstract

This work proposes a novel process of using pen tip and tail 3D trajectory for air signature. To acquire the trajectories we developed a new pen tool and a stereo camera was used. We proposed SliT-CNN, a novel 2D spatial-temporal convolutional neural network (CNN) for better featuring of the air signature. In addition, we also collected an air signature dataset from $45$ signers. Skilled forgery signatures per user are also collected. A detailed benchmarking of the proposed dataset using existing techniques and proposed CNN on existing and proposed dataset exhibit the effectiveness of our methodology.

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Code Implementations(2)

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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

00Shell, PythonMar 18, 20251 years agoMIT
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Cite this paper

@article{atreya2024enhancing,
  title  = {Enhancing 3D-Air Signature by Pen Tip Tail Trajectory Awareness: Dataset and Featuring by Novel Spatio-temporal CNN},
  author = {Saurabh Atreya and Maheswar Bora and Aritra Mukherjee and Abhijit Das},
  year   = {2024},
  eprint = {2401.02649},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.02649v1}
}

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