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 language | English |
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Title of host publication | ICAIP 2019 - 2019 3rd International Conference on Advances in Image Processing |
Publisher | Association for Computing Machinery |
Pages | 190-195 |
Number of pages | 6 |
ISBN (Electronic) | 9781450376754 |
DOIs | |
Publication status | Published - 2019 Nov 3 |
Event | 3rd International Conference on Advances in Image Processing, ICAIP 2019 - Chengdu, China Duration: 2019 Nov 8 → 2019 Nov 10 |
Publication series
Name | ACM International Conference Proceeding Series |
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Conference
Conference | 3rd International Conference on Advances in Image Processing, ICAIP 2019 |
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Country | China |
City | Chengdu |
Period | 19/11/8 → 19/11/10 |
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