Signature forgery is at the centre of several fraudulent activities and legal battles. The introduction of 3D signatures, the virtual signing of ones name in the air, has the potential to restrict forgers due to the absence of visual cues that can be easily copied. Existing 3D signature recognition approaches, however, have not leveraged the inherent spatial and temporal information, making it difficult to handle the diminished separability and reproducibility of these signatures. In this paper, we propose a novel spatio-temporal adaptation of the Siamese Neural Network, wherein one branch extracts spatial features using a 1D Convolutional Neural Network (CNN) while the other processes the input in the temporal domain using Long Short-Term Memory networks (LSTMs). Unlike conventional deep learning networks, Siamese networks are an application of One-Shot Learning so as to learn from a small amount of data as is often the case in real life problems. They employ a distance metric that is forced to be small for like samples (signatures from the same person), and large for different samples (from different persons). The proposed approach, termed ST-SNN, is compared to other baseline classification architectures, and demonstrated using a publicly available biometric 3D signature benchmark dataset, yielding True Positive Rate (TPR) of 94.63% with 4.1% False Acceptance Rate (FAR).

签名伪造是几次欺诈活动和法律斗争的中心。3D签名（名称中的虚拟签名）的引入，由于缺乏易于复制的视觉提示，因此有可能限制伪造者。但是，现有的3D签名识别方法尚未利用固有的空间和时间信息，因此难以处理这些签名的递减可分离性和可再现性。在本文中，我们提出了一种暹罗神经网络的新型时空适应方法，其中一个分支使用一维卷积神经网络（CNN）提取空间特征，而另一个分支使用长短期记忆网络处理时域中的输入（LSTM）。与传统的深度学习网络不同，连体网络是“一键式学习”的一种应用，可以从少量数据中学习，这在现实生活中经常会出现问题。他们采用的距离度量标准对于相似的样本（来自同一个人的签名）必须很小，而对于不同的样本（来自不同的人）则必须很大。所提出的称为ST-SNN的方法与其他基线分类体系结构进行了比较，并使用了公开提供的生物特征3D签名基准数据集进行了演示，得出的真实阳性率（TPR）为94.63％，错误接受率（FAR）为4.1％。