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

doi:10.1109/ICIP.2019.8803371

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

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (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 language	English
Title of host publication	2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
Publisher	IEEE Computer Society
Pages	2586-2590
Number of pages	5
ISBN (Electronic)	9781538662496
DOIs	https://doi.org/10.1109/ICIP.2019.8803371
Publication status	Published - 2019 Sept
Event	26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China Duration: 2019 Sept 22 → 2019 Sept 25

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	2019-September
ISSN (Print)	1522-4880

Conference

Conference	26th IEEE International Conference on Image Processing, ICIP 2019
Country/Territory	Taiwan, Province of China
City	Taipei
Period	19/9/22 → 19/9/25

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

Access to Document

10.1109/ICIP.2019.8803371

Cite this

Jung, J., Kim, J., & Toh, K. A. (2019). Transfer Learning of Wi-Fi Handwritten Signature Signals for Identity Verification based on the Kernel and the Range Space Projection. In 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings (pp. 2586-2590). [8803371] (Proceedings - International Conference on Image Processing, ICIP; Vol. 2019-September). IEEE Computer Society. https://doi.org/10.1109/ICIP.2019.8803371

Jung, Junsik ; Kim, Jooyoung ; Toh, Kar Ann. / Transfer Learning of Wi-Fi Handwritten Signature Signals for Identity Verification based on the Kernel and the Range Space Projection. 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings. IEEE Computer Society, 2019. pp. 2586-2590 (Proceedings - International Conference on Image Processing, ICIP).

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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.",

author = "Junsik Jung and Jooyoung Kim and Toh, {Kar Ann}",

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: {\textcopyright} 2019 IEEE.; 26th IEEE International Conference on Image Processing, ICIP 2019 ; Conference date: 22-09-2019 Through 25-09-2019",

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Jung, J, Kim, J & Toh, KA 2019, Transfer Learning of Wi-Fi Handwritten Signature Signals for Identity Verification based on the Kernel and the Range Space Projection. in 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings., 8803371, Proceedings - International Conference on Image Processing, ICIP, vol. 2019-September, IEEE Computer Society, pp. 2586-2590, 26th IEEE International Conference on Image Processing, ICIP 2019, Taipei, Taiwan, Province of China, 19/9/22. https://doi.org/10.1109/ICIP.2019.8803371

Transfer Learning of Wi-Fi Handwritten Signature Signals for Identity Verification based on the Kernel and the Range Space Projection. / Jung, Junsik; Kim, Jooyoung; Toh, Kar Ann.
2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings. IEEE Computer Society, 2019. p. 2586-2590 8803371 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2019-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - 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.

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AB - 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.

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Jung J, Kim J, Toh KA. Transfer Learning of Wi-Fi Handwritten Signature Signals for Identity Verification based on the Kernel and the Range Space Projection. In 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings. IEEE Computer Society. 2019. p. 2586-2590. 8803371. (Proceedings - International Conference on Image Processing, ICIP). doi: 10.1109/ICIP.2019.8803371

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

Abstract

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