research-article

Wi-Fi Based Handwritten Signature Verification Using a Triplet Network

Authors:

Young-Woong Kwon,

Kar-Ann TohAuthors Info & Claims

ICAIP '19: Proceedings of the 2019 3rd International Conference on Advances in Image Processing

Pages 190 - 195

https://doi.org/10.1145/3373419.3373434

Published: 24 January 2020 Publication History

Abstract

Attributed to the omnipresence of the radio signals for communications, sensing and recognition utilizing the Wi-Fi signals has significant advantage in terms of accessibility over conventional sensing means such as the camera. However, utilizing the raw Wi-Fi signals to capture in-air handwritten signatures for identity verification is yet a challenging task. In this paper, we propose a system for identity verification based on the handwritten signature signals captured by the Wi-Fi Channel State Information (CSI). A triplet network is adopted to learn the correlation between the captured signals and the user identities. To facilitate a fast converging loss model, a kernel and the range space learning is initially adopted for mining the triplet inputs. Subsequently, the triplet network is trained on a ConvNet structure based on the mined triplet inputs. Our experiments on a Wi-Fi dataset collected in-house show encouraging verification accuracy with faster training loss convergence comparing with that of the baseline triplet network and the Siamese network.

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Cited By

Ye MQu XHuang JWu X(2022)In-air Handwriting System Based on Improved YOLOv5 algorithm and Monocular Camera2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI56018.2022.00145(949-954)Online publication date: Oct-2022
https://doi.org/10.1109/ICTAI56018.2022.00145

Index Terms

Wi-Fi Based Handwritten Signature Verification Using a Triplet Network
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Security and privacy
  1. Security services
    1. Authentication
      1. Biometrics

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ICAIP '19: Proceedings of the 2019 3rd International Conference on Advances in Image Processing

November 2019

232 pages

ISBN:9781450376754

DOI:10.1145/3373419

Copyright © 2019 ACM.

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Southwest Jiaotong University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 January 2020

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ICAIP 2019

ICAIP 2019: 2019 3rd International Conference on Advances in Image Processing

November 8 - 10, 2019

Chengdu, China

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Cited By

Ye MQu XHuang JWu X(2022)In-air Handwriting System Based on Improved YOLOv5 algorithm and Monocular Camera2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI56018.2022.00145(949-954)Online publication date: Oct-2022
https://doi.org/10.1109/ICTAI56018.2022.00145

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