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.
|Title of host publication||2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|Publication status||Published - 2019 Sep|
|Event||26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China|
Duration: 2019 Sep 22 → 2019 Sep 25
|Name||Proceedings - International Conference on Image Processing, ICIP|
|Conference||26th IEEE International Conference on Image Processing, ICIP 2019|
|Country||Taiwan, Province of China|
|Period||19/9/22 → 19/9/25|
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 (Grant number: NRF-2018R1D1A1A09081956).
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Signal Processing