Transfer Learning of Wi-Fi Handwritten Signature Signals for Identity Verification based on the Kernel and the Range Space Projection

Junsik Jung, Jooyoung Kim, Kar Ann Toh

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

1 Citation (Scopus)

Abstract

In this paper, we propose a system for identity verification based on the gesture signals of handwritten signature captured by the Wi-Fi CSI wave packets at different positions using transfer learning. Essentially, a ConvNet is first pretrained using the Wi-Fi signature signals collected from one position. Subsequently, the pretrained feature extractor is transferred to recognize signals collected from another position via a rapid retraining process. We utilize the kernel and the range space projection learning when we retrain the transferred model. Our experimental results on an in-house Wi-Fi handwritten signature signal dataset show that the signature signals from the new position can be effectively classified without needing to retrain the model from scratch.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Pages2586-2590
Number of pages5
ISBN (Electronic)9781538662496
DOIs
Publication statusPublished - 2019 Sep
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
Duration: 2019 Sep 222019 Sep 25

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
CountryTaiwan, Province of China
CityTaipei
Period19/9/2219/9/25

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 (Grant number: NRF-2018R1D1A1A09081956).

Publisher Copyright:
© 2019 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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