Wi-Fi based handwritten signature verification using a triplet network

Young Woong Kwon, Jooyoung Kim, Kar Ann Toh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationICAIP 2019 - 2019 3rd International Conference on Advances in Image Processing
PublisherAssociation for Computing Machinery
Pages190-195
Number of pages6
ISBN (Electronic)9781450376754
DOIs
Publication statusPublished - 2019 Nov 3
Event3rd International Conference on Advances in Image Processing, ICAIP 2019 - Chengdu, China
Duration: 2019 Nov 82019 Nov 10

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Advances in Image Processing, ICAIP 2019
CountryChina
CityChengdu
Period19/11/819/11/10

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2018R1D1A1A09081956).

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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