mmSign: mmWave-based Few-Shot Online Handwritten Signature Verification-Reference-Cited by-同舟云学术

mmSign: mmWave-based Few-Shot Online Handwritten Signature Verification

Published:2023-06-24 Issue: Volume: Page:
ISSN:1550-4859
Container-title:ACM Transactions on Sensor Networks
language:en
Short-container-title:ACM Trans. Sen. Netw.

Author:

Han Mingda¹,Yang Huanqi²,Ni Tao²,Duan Di²,Ruan Mengzhe²,Chen Yongliang²,Zhang Jia³,Xu Weitao²

Affiliation:

1. Shandong Normal University, China and City University of Hong Kong Shenzhen Research Institute, China

2. City University of Hong Kong, China and City University of Hong Kong Shenzhen Research Institute, China

3. Shandong Normal University, China

Abstract

Handwritten signature verification has become one of the most important document authentication methods that are widely used in the financial, legal, and administrative sectors. Compared to offline methods based on static signature images, online handwritten signature verification methods are more reliable because of the temporary dynamic information (e.g., signing velocity, writing force, stroke order) that alleviates the risk of being forged. However, most existing online handwritten signature verification solutions are reliant on specific signing devices (e.g., customized pens or writing pads) and require extensive data collection during the registration phase, resulting in poor adaptability and applicability for new users. In this paper, we propose mmSign, a millimeter wave (mmWave)-based online handwritten signature verification system, which enables accurate sensing of the user’s hand movements when signing through the superior sensing capability of mmWave. mmSign extracts the time-velocity feature maps from the captured mmWave signals by the carefully designed signal processing algorithms, and then exploits a transformer-based verification model for signature verification. In addition, a novel meta-learning strategy with proposed task generation and data augmentation methods is introduced in mmSign to teach the verification model to learn effectively with limited samples, allowing our model to quickly adapt to new users. Extensive experiments show that mmSign is a robust, efficient, and secure handwritten signature verification system, achieving 84.07%, 87.31%, 91.12%, and 96.54% verification accuracy when 1, 3, 5, and 10 labeled signatures are available, respectively, while being resistant to common forgery attacks.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications

Link

https://dl.acm.org/doi/pdf/10.1145/3605945

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