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.
|Title of host publication||ICAIP 2019 - 2019 3rd International Conference on Advances in Image Processing|
|Publisher||Association for Computing Machinery|
|Number of pages||6|
|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
|Name||ACM International Conference Proceeding Series|
|Conference||3rd International Conference on Advances in Image Processing, ICAIP 2019|
|Period||19/11/8 → 19/11/10|
Bibliographical noteFunding 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).
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
© 2019 Association for Computing Machinery.
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Networks and Communications