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首页> 外文会议>RIAO/OPTILAS 2004 >Verification of the authenticity of handwritten signature using structure neural network type OCON
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Verification of the authenticity of handwritten signature using structure neural network type OCON

机译：使用结构神经网络类型OCON验证手写签名的真实性

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摘要

A method in order to carry out the verification of handwritten signatures is described. The method keeps in mind global features and local features that encode the shape and the dynamics of the signatures. Signatures are recorded with a digital tablet that can read the position and pressure of the pen. Input patterns are considered time and space dependent. Before extracting the information of the static features such as total length or height/width ratio, and the dynamic features such as speed or acceleration, the signature is normalized for position, size and orientation using its Fourier Descriptors. The comparison stage is carried out for algorithms of neurals networks. For each one of the sets of features a special two stage Perceptron OCON (one-class-one-network) classification structure has been implemented. In the first stage networks multilayer perceptron with few neurons are used. The classifier combines the decision results of the neural networks and the Euclidean distance obtained using the two feature sets. The results of the first-stage classifier feed a second-stage radial basis function (RBF) neural network structure, which makes the final decision. The entire system was extensively tested, 160 neurals networks has been implemented.
机译：描述了一种用于执行手写签名的验证的方法。该方法牢记对签名的形状和动态进行编码的全局特征和局部特征。用数字平板电脑记录签名，该数字平板电脑可以读取笔的位置和压力​​。输入模式被认为是时间和空间依赖的。在提取静态特征（例如总长度或高度/宽度比）以及动态特征（例如速度或加速度）的信息之前，请使用其傅立叶描述符对签名进行位置，大小和方向的标准化。比较阶段是针对神经网络算法进行的。对于每组功能，都实现了特殊的两阶段Perceptron OCON（一类一网络）分类结构。在第一阶段网络中，使用了几乎没有神经元的多层感知器。分类器将神经网络的决策结果和使用两个特征集获得的欧几里得距离相结合。第一级分类器的结果将馈入第二级径向基函数（RBF）神经网络结构，这将做出最终决定。整个系统经过了广泛的测试，已经实现了160个神经网络。

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