Optimized SVM Parameters with Googlenet Model for Handwritten Signature Verification

被引:0
|
作者
Tamilarasi, K. [1 ]
Annapoorani, V. [2 ]
Nathiya, T. [1 ]
机构
[1] Excel Engn Coll, Dept ECE, Namakkal, Tamilnadu, India
[2] Mahendra Inst Technol, Dept ECE, Namakkal, India
关键词
Handwritten Signature; Convolutional Neural Network; Inception V1; CEDAR; BHSig260 signature corpus; and UTSig;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
- As far as behavioral biometric authentication procedures go, signature verification is at the top of the list. The most popular form of user authentication, a signature is like a "seal of approval" that confirms the user's permission. The primary objective of this verification technique is to distinguish between real signatures and those that have been forgeried by imposters. In order to learn characteristics from the pre-processed real and fake signatures, Convolutional Neural Networks (CNN) were used in this research. The model utilized to train the CNN was the Inception V1 architecture (GoogleNet). To make the network larger rather than deeper, the design makes advantage of the idea of having various filters on the same level. A small number of publicly accessible datasets, including CEDAR, the BHSig260 signature corpus, as well as UTSig, are used to evaluate the suggested approach in this study.
引用
收藏
页码:1060 / 1072
页数:13
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