Writer-Independent Handwritten Signature Verification based on One-Class SVM Classifier

被引：0

作者：

Guerbai, Y. ^{[1
]}

Chibani, Y. ^{[1
]}

Hadjadji, B. ^{[1
]}

机构：

[1] Univ Sci & Technol HouariBoumediene USTHB, Fac Elect & Comp Sci, Speech Commun & Signal Proc Lab, 32 El Alia, Algiers 16111, Algeria

来源：

PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2014年

关键词：

One-class support vector machines; hard and soft threshold; signature verification; curvelet transform; RECOGNITION; SUPPORT;

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：

The limited number of writers and the lack of forgeries as counterexample to construct the systems is the main difficulty task for designing a robust off-line Handwritten Signature Verification System (HSVS). In this paper, we propose to study the influence of writer's number using conjointly the curvelet transform and the One-Class Support Vector Machine (OC-SVM), which takes in consideration only genuine signatures. The design of the HSVS is based on the writer-independent approach. Experimental results conducted on the standard CEDAR and GPDS datasets demonstrate that the proposed method allows achieving the lowest Average Error Rate with a limited number of writers.

引用

页码：313 / 317

页数：5

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