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).

对于签名验证系统，可以将微变形定义为签名的相同笔划中的细微差异或不同签名者的特殊书写习惯。这些微小的变形可以揭示出真正的签名和熟练的伪造之间的核心区别。在本文中，我们证明了卷积神经网络（CNN）有潜力通过最大池提取这些微变形。更具体地，可以通过观察最大池的池窗口中的最大值的位置坐标来确定微变形。广泛的分析和实验表明，通过将此位置信息用作捕获微变形以及卷积特征的新特征，可以实现最新的性能。