RankSVM for Offline Signature Verification
RankSVM for Offline Signature Verification
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DOI：
10.1109/icdar.2019.00153
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Yan Zheng;Yuchen Zheng;W. Ohyama;D. Suehiro;S. Uchida
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文献类型：
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作者：
Yan Zheng;Yuchen Zheng;W. Ohyama;D. Suehiro;S. Uchida
签名验证系统存在学习不平衡的问题，这对分类器提出了严格的要求。标准的分类方法，如支持向量机，往往会降低不平衡数据的性能或需要额外的参数来平衡数据。在这项研究中，作为一种新的签名验证方法，我们使用RankSVM作为作者相关的分类器，从理论上保证了不平衡数据的泛化性能。为了研究RankSVM解决签名验证任务中不平衡学习问题的能力，对GPDS-150，GPDS-300，GPDS-600和GPDS-1000数据集的位图和GPDS-960数据集的深度特征进行了广泛的实验。实验结果表明，基于RankSVM的方法在GPDS-960数据集的深度特征上获得了与最先进方法几乎相当的性能，并且在GPDS-150，GPDS-300，GPDS-600和GPDS-1000数据集的位图上获得了比基于标准SVM的方法更好的性能。
Signature verification systems suffer from imbalanced learning, which imposes strict requirements on classifiers. The standard classification approaches, such as SVM, often degrade the performance for imbalanced data or require additional parameters for data balancing. In this study, as a new approach for signature verification, we use RankSVM as the writer-dependent classifiers, which theoretically guarantees the generalization performance for imbalanced data. To investigate the ability of RankSVM for solving imbalanced learning problems in signature verification tasks, the extensive experiments are conducted on bitmaps of GPDS-150, GPDS-300, GPDS-600, and GPDS-1000 datasets and deep features of GPDS-960 dataset. The experimental results demonstrate that the RankSVM-based approach obtains a nearly equivalent performance with the state-of-the-art method on deep features of the GPDS-960 dataset, and achieves significantly better performance than standard-SVM-based approach on bitmaps of GPDS-150, GPDS-300, GPDS-600, and GPDS-1000 datasets.