Online Signature Verification Using Multi-Distance Measures and Weighting with Gradient Boosting
Online Signature Verification Using Multi-Distance Measures and Weighting with Gradient Boosting
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DOI：
10.1109/lifetech.2019.8884008
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发表时间：
2019-03
期刊：
影响因子：
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通讯作者：
Manabu Okawa
中科院分区：
文献类型：
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作者：
Manabu Okawa
为了提高在线签名验证的性能，同时保持较低的计算成本和较高的安全性，该研究提出了一种新的单模板策略，在基于函数的方法。具体来说，我们采用动态时间规整（DTW）重心平均，以获得一个有效的平均模板，同时保留所有参考之间的用户内的变化。然后，通过使用平均模板，我们计算多个距离的措施：多DTW从每个功能与独立的翘曲和单一DTW从所有功能与相关的翘曲。为了提高鉴别能力，我们应用了一种使用梯度提升的加权方案来有效地联合收割机组合多距离测量。通过在一个流行的SVC2004 Task2数据集上的应用，证明了该算法的良好性能。
To improve performance of online signature verification while maintaining lower calculation cost and higher security, this study proposes a novel single-template strategy in function-based approaches. Specifically, we adopt dynamic time warping (DTW) barycenter averaging to obtain an effective mean template while preserving intra-user variability between all references. Then, by using the mean template, we calculate multi-distance measures: The multiple DTW from each feature with independent warping and the single DTW from all features with dependent warping. To boost the discriminative power, we apply a weighting scheme using gradient boosting to efficiently combine the multi-distance measures. The promising performance is demonstrated through its application to a popular SVC2004 Task2 dataset.