Capturing Micro Deformations from Pooling Layers for Offline Signature Verification
Capturing Micro Deformations from Pooling Layers for Offline Signature Verification
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DOI：
10.1109/icdar.2019.00180
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发表时间：
2019-09
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通讯作者：
Yuchen Zheng;W. Ohyama;Brian Kenji Iwana;S. Uchida
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文献类型：
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作者：
Yuchen Zheng;W. Ohyama;Brian Kenji Iwana;S. Uchida
在本文中，我们提出了一种基于卷积神经网络（CNN）的新方法，该方法提取最大池化操作中最大值的位置信息（位移特征），并将其与池化特征融合，以捕获真实签名和熟练伪造签名之间的微变形作为特征提取过程。在特征提取之后，我们将支持向量机（svm）作为每个用户的作者依赖分类器来构建签名验证系统。在GPDS-150、GPDS-300、GPDS-1000、GPDS-2000和GPDS-5000数据集上的大量实验结果表明，该方法可以很好地区分真实签名和相应的熟练伪造签名，并在这些数据集上取得了最先进的结果。
In this paper, we propose a novel Convolutional Neural Network (CNN) based method that extracts the location information (displacement features) of the maximums in the max-pooling operation and fuses it with the pooling features to capture the micro deformations between the genuine signatures and skilled forgeries as a feature extraction procedure. After the feature extraction procedure, we apply support vector machines (SVMs) as writer-dependent classifiers for each user to build the signature verification system. The extensive experimental results on GPDS-150, GPDS-300, GPDS-1000, GPDS-2000, and GPDS-5000 datasets demonstrate that the proposed method can discriminate the genuine signatures and their corresponding skilled forgeries well and achieve state-of-the-art results on these datasets.