IEEE Trans Cybern. 2018 Jan;48(1):228-239. doi: 10.1109/TCYB.2016.2630419. Epub 2016 Dec 6.
The dynamic signature is a biometric trait widely used and accepted for verifying a person's identity. Current automatic signature-based biometric systems typically require five, ten, or even more specimens of a person's signature to learn intrapersonal variability sufficient to provide an accurate verification of the individual's identity. To mitigate this drawback, this paper proposes a procedure for training with only a single reference signature. Our strategy consists of duplicating the given signature a number of times and training an automatic signature verifier with each of the resulting signatures. The duplication scheme is based on a sigma lognormal decomposition of the reference signature. Two methods are presented to create human-like duplicated signatures: the first varies the strokes' lognormal parameters (stroke-wise) whereas the second modifies their virtual target points (target-wise). A challenging benchmark, assessed with multiple state-of-the-art automatic signature verifiers and multiple databases, proves the robustness of the system. Experimental results suggest that our system, with a single reference signature, is capable of achieving a similar performance to standard verifiers trained with up to five signature specimens.
动态签名是一种广泛使用和接受的生物识别特征，用于验证个人的身份。当前基于自动签名的生物识别系统通常需要五、十甚至更多个人签名样本，以学习足够的个体内变异性，从而提供个人身份的准确验证。为了减轻这一缺点，本文提出了一种仅使用单个参考签名进行训练的方法。我们的策略包括复制给定签名的次数，并使用每个生成的签名来训练自动签名验证器。复制方案基于参考签名的 sigma 对数正态分解。本文提出了两种创建类似人类的重复签名的方法：第一种方法改变了笔画的对数正态参数（逐笔画），而第二种方法则修改了它们的虚拟目标点（逐目标）。使用多个最先进的自动签名验证器和多个数据库进行评估的具有挑战性的基准证明了该系统的稳健性。实验结果表明，我们的系统仅使用单个参考签名，就能够实现与使用多达五个签名样本训练的标准验证器相当的性能。
