基于特征不变矩的签名鉴别方案

脱机手写签名鉴别的主要困难在于有效特征的提取,因此本文主要围绕提取能反映签名本质的特征进行了相关研究。在具体解决签名鉴别时,一方面要考虑签名的静态特征,另一方面寻找动态特征。重点研究了静态特征。提取静态特征时,利用伪Zernike矩的尺度及位移不变性,计算签名图像的0~10阶伪Zernike矩来组成特征向量。在此基础上,对基于上述两种不同特征的加权欧氏距离分类器进行性能比较,并找到了一个有效的数据融合方案。

Signature Verification Based on Feature Invariant Moments

The main difficulty of offline handwritten Chinese signature verification is feature extraction.So how to extract features based on characteristics of Chinese signature is discussed in this dissertation.When signature verification is considered,there are two problems needed to solve.One is static feature extraction.The other is seeking dynamic features.And this is mainly concerned in this dissertation.When static features are extracted,they are described using the translation and scale invariant pseudo-Zernike moments,the feature vectors are composed of pseudo-Zernike moments from order 0 to 10.The dynamic features are described using parameterized Hough Transform.Based on former research,two simple Euclidian distance classifier are used to evaluate the performance of the two proposed methods,and an effective data fusion scheme is proposed.