mmHSV: In-Air Handwritten Signature Verification via Millimeter-wave Radar

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Wanqing Li, Tongtong He, Nan Jing, Lin Wang
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引用次数: 0

Abstract

Electronic signatures are widely used in financial business, telecommuting and identity authentication. Offline electronic signatures are vulnerable to copy or replay attacks. Contact-based online electronic signatures are limited by indirect contact such as handwriting pads and may threaten the health of users. Consider combining hand shape features and writing process features to form electronic signatures, the paper proposes an in-air handwritten signature verification system with millimeter-wave(mmWave) radar, namely mmHSV. First, the biometrics of the handwritten signature process are modeled, and phase-dependent biometrics and behavioral features are extracted from the mmWave radar mixture signal. Secondly, a handwritten feature recognition network based on few-sample learning is presented to fuse multi-dimensional features and determine user legitimacy. Finally, mmHSV is implemented and evaluated with commercial mmWave devices in different scenarios and attack mode conditions. Experimental results show that the mmHSV can achieve accurate, efficient, robust and scalable handwritten signature verification. Area Under Curve (AUC) is 98.96 \(\% \) , False Acceptance Rate (FAR) is 5.1 \(\% \) at the fixed threshold, AUC is 97.79 \(\% \) for untrained users.
mmHSV:基于毫米波雷达的空中手写签名验证
电子签名广泛应用于金融业务、远程办公和身份认证等领域。离线电子签名容易受到复制或重放攻击。基于接触的在线电子签名受到手写板等间接接触的限制,可能威胁用户的健康。考虑结合手形特征和书写过程特征形成电子签名,本文提出了一种利用毫米波(mmWave)雷达的空中手写签名验证系统,即mmHSV。首先,对手写签名过程的生物特征进行建模,并从毫米波雷达混合信号中提取相位相关的生物特征和行为特征。其次,提出了一种基于少样本学习的手写体特征识别网络,融合多维特征,确定用户合法性;最后,利用商用毫米波器件在不同场景和攻击模式条件下实现和评估mmHSV。实验结果表明,mmHSV可以实现准确、高效、鲁棒和可扩展的手写签名验证。曲线下面积(AUC)为98.96 \(\% \),固定阈值下的错误接受率(FAR)为5.1 \(\% \),未经训练的用户的AUC为97.79 \(\% \)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.20
自引率
3.70%
发文量
0
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