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
PublisherICST
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

NamePervasiveHealth: Pervasive Computing Technologies for Healthcare
ISSN (Print)2153-1633

Conference

Conference3rd International Conference on Advances in Image Processing, ICAIP 2019
Country/TerritoryChina
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

Publisher Copyright:
© 2019 Association for Computing Machinery.

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Computer Science Applications
  • Health Informatics

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