Template Matching Using Time-Series Averaging and DTW With Dependent Warping for Online Signature Verification
Template Matching Using Time-Series Averaging and DTW With Dependent Warping for Online Signature Verification
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DOI：
10.1109/access.2019.2923093
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Okawa, Manabu
中科院分区：
文献类型：
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作者：
Okawa, Manabu
在线签名认证在生物识别和取证中有着广泛的应用。针对当前大数据时代对高速系统的需求，为同时提高系统性能和计算复杂度，提出了一种基于单模板的动态时间规整（DTW）和相关规整的在线签名验证策略，并尝试构建一种新的基于欧氏重心的DTW重心平均（EB-DBA）时间序列平均方法。具体而言，本研究提出了一种单模板策略，使用EB-DBA创建的平均模板，以实现更高的性能，在较低的计算复杂度的在线签名验证。该方法的鉴别力增强后，探索两个DTW的翘曲类型，其中发现，DTW与依赖翘曲表现出更好的性能。实验结果表明，该方法在降低错误率和计算复杂度的同时，有效地解决了在线签名验证问题。
Online signature verification has been widely applied in biometrics and forensics. Due to the recent demand on high-speed systems in this era of big data, to simultaneously improve its performance and calculation complexity, this study focuses on a single-template strategy that uses dynamic time warping (DTW) with dependent warping for online signature verification, and attempts to construct a novel time-series averaging method called Euclidean barycenter-based DTW barycenter averaging (EB-DBA). Specifically, this study proposes a single-template strategy using a mean template created by the EB-DBA to achieve higher performance at lower calculation complexity for online signature verification. The method's discriminative power is enhanced upon the exploration of two DTW warping types, where it is found that the DTW with dependent warping exhibits better performance. The popular MCYT-100 dataset is utilized in the experiments, which confirms the effectiveness of the proposed method in simultaneously achieving lower error rate and lower calculation complexity, for online signature verification.