Neural Network-based Handwritten Signature Verification

被引:38
作者
McCabe, Alan [1 ]
Trevathan, Jarrod [1 ]
Read, Wayne [1 ]
机构
[1] James Cook Univ, Sch Math Phys & Informat Technol, Townsville, Qld, Australia
关键词
Network topology;
D O I
10.4304/jcp.3.8.9-22
中图分类号
TP39 [计算机的应用];
学科分类号
081203 [计算机应用技术]; 0835 [软件工程];
摘要
Handwritten signatures are considered as the most natural method of authenticating a person's identity (compared to other biometric and cryptographic forms of authentication). The learning process inherent in Neural Networks (NN) can be applied to the process of verifying handwritten signatures that are electronically captured via a stylus. This paper presents a method for verifying handwritten signatures by using a NN architecture. Various static (e.g., height, slant, etc.) and dynamic (e.g., velocity, pen tip pressure, etc.) signature features are extracted and used to train the NN. Several Network topologies are tested and their accuracy is compared. The resulting system performs reasonably well with an overall error rate of 3.3 % being reported for the best case.
引用
收藏
页码:9 / 22
页数:14
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