IEEE Trans Pattern Anal Mach Intell. 2017 May;39(5):951-964. doi: 10.1109/TPAMI.2016.2560810. Epub 2016 Apr 29.
Biometric researchers have historically seen signature duplication as a procedure relevant to improving the performance of automatic signature verifiers. Different approaches have been proposed to duplicate dynamic signatures based on the heuristic affine transformation, nonlinear distortion and the kinematic model of the motor system. The literature on static signature duplication is limited and as far as we know based on heuristic affine transforms and does not seem to consider the recent advances in human behavior modeling of neuroscience. This paper tries to fill this gap by proposing a cognitive inspired algorithm to duplicate off-line signatures. The algorithm is based on a set of nonlinear and linear transformations which simulate the human spatial cognitive map and motor system intra-personal variability during the signing process. The duplicator is evaluated by increasing artificially a training sequence and verifying that the performance of four state-of-the-art off-line signature classifiers using two publicly databases have been improved on average as if we had collected three more real signatures.
生物识别研究人员历来将签名复制视为与提高自动签名验证器性能相关的过程。已经提出了不同的方法来基于启发式仿射变换、非线性失真和运动系统的运动学模型来复制动态签名。关于静态签名复制的文献有限，据我们所知，它基于启发式仿射变换，似乎没有考虑到神经科学中最近在人类行为建模方面的进展。本文试图通过提出一种认知启发式算法来复制离线签名来填补这一空白。该算法基于一组非线性和线性变换，这些变换模拟了人类在签名过程中的空间认知图和运动系统个体内变异性。通过人为增加训练序列并验证使用两个公共数据库的四个最先进的离线签名分类器的性能得到了提高，就好像我们多收集了三个真实签名一样，对复制器进行了评估。
