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Online signature verification by continuous wavelet transformation of speed signals

机译:通过速度信号的连续小波变换进行在线签名验证

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Despite the imitability of the signatures due to presence of numerous image processing programs, online verification systems could provide sufficient security for e-signatures. Recent developments in touch screen technology and android programming also lead to utilization of hidden interfaces stealthily collecting the unique characteristics and storing the key features aside from geometrics. Therefore, we initially designed a signing interface for touchscreens which stealthily collects the precise coordinates while an individual is signing on the screen by fingertips. Even if the coordinate data is extracted as a matrix consisting of x and y values with corresponding time, the speed array is consequently calculated to investigate the higher frequency regions. The speed data processed by continuous wavelet transformations (CWT) to reveal the frequency information of the signing speed with respect to time information. The grayscale spectrograms created by wavelet transforms are converted into arrays for subsequent training session performed by support vector machines (SVM). The trained network successfully classified further attempts of the real and fake signatures with 1.67% false negative (FNR), 3.33% false positive rates (FPR) and 3.41% equal error rate (EER) for 120 signatures, even though the signature is totally public. For understanding the validity of the CWT and SVM running consecutively, the experiments are re-conducted for the signatures taken from SVC2004 and SUSIG public databases. (C) 2018 Elsevier Ltd. All rights reserved.
机译:尽管由于存在大量图像处理程序而使签名具有可模仿性,但在线验证系统仍可以为电子签名提供足够的安全性。触摸屏技术和android编程的最新发展也导致了隐藏界面的利用,它们隐秘地收集了独特的特征并存储了除几何图形之外的关键特征。因此,我们最初设计了一种用于触摸屏的签名界面,当一个人用指尖在屏幕上签名时,该界面会秘密收集精确的坐标。即使将坐标数据提取为包含x和y值并具有相应时间的矩阵,也可以计算速度数组以研究较高频率的区域。速度数据通过连续小波变换(CWT)处理,以显示相对于时间信息的签名速度的频率信息。由小波变换创建的灰度频谱图被转换为数组,以用于由支持向量机(SVM)执行的后续训练会话。训练有素的网络成功地对真实签名和假签名进行了进一步的尝试分类,即使签名是完全公开的,对于120个签名,假阴性(FNR),假阳性率(FPR)为3.33%和均等错误率(EER)为3.41% 。为了了解连续运行的CWT和SVM的有效性,对从SVC2004和SUSIG公共数据库获取的签名进行了重新实验。 (C)2018 Elsevier Ltd.保留所有权利。

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