On-Line Signature Verification Based on Genetic Optimization and Neural-Network-Driven Fuzzy Reasoning

被引：0

作者：

Cesar Martinez-Romo, Julio ^{[1
]}

Javier Luna-Rosas, Francisco ^{[1
]}

Mora-Gonzalez, Miguel ^{[2
]}

机构：

[1] Inst Technol Aguascalientes, Dept Elect Engn, Av A Lopez Mateos 1801 Ote Col Bona Gens, Aguascalientes 20256, Ags, Mexico

[2] Univ Guadalajara, Univ Ctr Los Lagos, Lagos De Moreno 47460, Jalisco, Mexico

来源：

MICAI 2009: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2009年 / 5845卷

关键词：

Signature verification; fuzzy reasoning; GA; neural networks;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents an innovative approach to solve the on-line signature verification problem in the presence of skilled forgeries. Genetic algorithms (GA) and fuzzy reasoning are the core of our solution. A standard GA is used to hod a near optimal representation of the features of a signature to minimize the risk of accepting skilled forgeries. Fuzzy reasoning here is carried out by Neural Networks. The method of a human expert examiner of questioned signatures is adopted here. The solution was tested in the presence of genuine, random and skilled forgeries, with high correct verification rates.

引用

页码：246 / +

页数：3

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