Online signature verification using single-template matching with time-series averaging and gradient boosting
Online signature verification using single-template matching with time-series averaging and gradient boosting
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DOI：
10.1016/j.patcog.2020.107227
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发表时间：
2020-06
期刊：
影响因子：
--
通讯作者：
Manabu Okawa
中科院分区：
文献类型：
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作者：
Manabu Okawa
随着大数据时代人工智能的最新发展，人们对在线签名验证系统的需求是高速运行，提供高水平的安全性，在实现足够性能的同时允许高容差。针对这些需求，本研究提出了一种新颖的单模板策略，该策略使用平均模板集和加权多重动态时间翘曲（DTW）距离，用于基于函数的在线签名验证方法。具体而言，为了获得每个特征的有效均值模板，同时反映所有参考样本之间的用户内部可变性，我们采用了一种基于欧几里德重心的DTW重心平均的时间序列平均方法。然后，通过使用均值模板集，基于相关和独立翘曲计算多元时间序列的多个DTW距离。最后，为了提高判别能力，我们采用了一种基于梯度增强模型的加权方案来有效地组合多个DTW距离。在常用的SVC2004 Task1/Task2和MCYT-100签名数据集上的实验结果表明，该方法对在线签名验证是有效的。
In keeping with recent developments in artificial intelligence in the era of big data, there is a demand for online signature verification systems that operate at high speeds, provide a high level of security, and allow high tolerances while achieving sufficient performance. In response to these needs, the present study proposes a novel, single-template strategy using a mean template set and weighted multiple dynamic time warping (DTW) distances for a function-based approach to online signature verification. Specifically, to obtain an effective mean template for each feature while reflecting intra-user variability between all the reference samples, we adopt a novel time-series averaging method based on Euclidean barycenter-based DTW barycenter averaging. Then, by using the mean template set, we calculate multiple DTW distances from multivariate time series based on dependent and independent warping. Finally, to boost the discriminative power, we apply a weighting scheme using a gradient boosting model to efficiently combine the multiple DTW distances. Experimental results using the common SVC2004 Task1/Task2 and MCYT-100 signature datasets confirm that the proposed method is effective for online signature verification.