Learning the micro deformations by max-pooling for offline signature verification
Learning the micro deformations by max-pooling for offline signature verification
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DOI：
10.1016/j.patcog.2021.108008
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Yuchen Zheng;Brian Kenji Iwana;M. I. Malik;Sheraz Ahmed;W. Ohyama;S. Uchida
中科院分区：
文献类型：
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作者：
Yuchen Zheng;Brian Kenji Iwana;M. I. Malik;Sheraz Ahmed;W. Ohyama;S. Uchida
对于签名验证系统，微变形可以定义为不同签名者在相同的签名笔画上的微小差异或特殊的书写习惯。这些微小的变形可以揭示真正的签名和熟练的伪造之间的核心区别。在这篇文章中，我们证明了卷积神经网络(CNN)具有通过最大值池来提取这些微小变形的潜力。更具体地说，可以通过观察最大合并窗口中的最大值的位置坐标来确定微观变形。大量的分析和实验表明，通过使用这种位置信息作为捕捉微小变形的新特征以及卷积特征，可以获得最先进的性能。该方法在四个公开可用的不同语言的数据集上的性能优于最新的系统，即英语(GPDS合成，雪松)、波斯语(UTSig)和印地语(BHSig260)。
For signature verification systems, micro deformations can be defined as the small differences in the same strokes of signatures or special writing habits of different signers. These micro deformations can reveal the core distinction between the genuine signatures and skilled forgeries. In this paper, we prove that Convolutional Neural Networks (CNNs) have the potential to extract those micro deformations by max-pooling. More specifically, the micro deformations can be determined by watching the location coordinates of the maximum values in pooling windows of max-pooling. Extensive analysis and experiments demonstrate that it is possible to achieve state-of-the-art performance by using this location information as a new feature for capturing micro deformations, along with convolutional features. The proposed method outperforms the state-of-the-art systems on four publicly available datasets of different languages, i.e., English (GPDSsynthetic, CEDAR), Persian (UTSig), and Hindi (BHSig260).