基于改进LPP和ECOC-SVMS的离线签名识别方法  

Method of Off-line Signature Recognition Based on Improved LPP and ECOC-SVMS

作　　者：蒋青云[1] JIANC Qing-yun(hffornlation Center,Hunan Maternal and Child Health Care Center,Changsha 410008,China)

机构地区：[1]湖南省妇幼保健院信息中心,湖南长沙410008

出　　处：《计算机与现代化》2018年第10期74-78,共5页Computer and Modernization

摘　　要：提出一种基于改进LPP和ECOC-SVMS的离线签名识别方法。针对预处理后的签名图像,选择多种有效特征构建高维特征向量,引入一种改进的保局投影方法进行特征提取并同时实现高效降维;签名识别方面,使用基于Hadamard纠错编码方法的ECOC支持向量机多类分类方法,并引入近似概率对ECOC解码进行改进,以提升多类分类器的性能。实验结果表明此方法的可行性和有效性。A method of off-line signature recognition based on locality preserving projection（LPP） and Error Correcting Output Code support vector maehine（ECOC-SVMS） is proposed. After selecting nmltiple features from preproeessed signature images, high dimensionality feature vectors are constructed. Then, an improved LPP method is used to extract effect features and reduce dimensionality. A nmlti-elassifieation classifier based on Hadamard code ECOC-SVMS is used to deal with signature recognition problem. A proximate probability output of SVMS is employed to improve the decoding processing of ECOC framework to enhance the performance of nmlti-elassifieation. The experiment result shows that the proposed method is feasible and effective.

关 键 词：离线签名识别 保局投影 纠错编码支持向量机 

分 类 号：TP317.4[自动化与计算机技术—计算机软件与理论]