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Sen. Netw."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>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 with 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 article, we propose mmSign, a millimeter wave (mmWave)\u2013based online handwritten signature verification system, which enables accurate sensing of the user\u2019s 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.<\/jats:p>","DOI":"10.1145\/3605945","type":"journal-article","created":{"date-parts":[[2023,6,24]],"date-time":"2023-06-24T11:14:38Z","timestamp":1687605278000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["mmSign: mmWave-based Few-Shot Online Handwritten Signature Verification"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8566-2879","authenticated-orcid":false,"given":"Mingda","family":"Han","sequence":"first","affiliation":[{"name":"Shandong Normal University, Jinan, China and City University of Hong Kong Shenzhen Research Institute, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7867-6217","authenticated-orcid":false,"given":"Huanqi","family":"Yang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Kowloon, China and City University of Hong Kong Shenzhen Research Institute, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0671-3020","authenticated-orcid":false,"given":"Tao","family":"Ni","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Kowloon, China and City University of Hong Kong Shenzhen Research Institute, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4184-0762","authenticated-orcid":false,"given":"Di","family":"Duan","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Kowloon, China and City University of Hong Kong Shenzhen Research Institute, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0458-5751","authenticated-orcid":false,"given":"Mengzhe","family":"Ruan","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Kowloon, China and City University of Hong Kong Shenzhen Research Institute, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2483-8890","authenticated-orcid":false,"given":"Yongliang","family":"Chen","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Kowloon, China and City University of Hong Kong Shenzhen Research Institute, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2513-4115","authenticated-orcid":false,"given":"Jia","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shandong Normal University, Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9741-5912","authenticated-orcid":false,"given":"Weitao","family":"Xu","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Kowloon, China and City University of Hong Kong Shenzhen Research Institute, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2024,5,11]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"265","volume-title":"Proceedings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et\u00a0al. 2016. 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In Proceedings of the Annual International Conference on Mobile Computing and Networking (MobiCom). 82\u201394."},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2017.206"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3196143"},{"key":"e_1_3_2_70_2","first-page":"64","volume-title":"Proceedings of the IEEE International Conference on Sensing, Communication, and Networking (SECON)","author":"Wang Yao","year":"2022","unstructured":"Yao Wang, Tao Gu, Tom H. Luan, and Yong Yu. 2022. Your breath doesn\u2019t lie: Multi-user authentication by sensing respiration using mmWave radar. 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