One-Class Arabic Signature Verification: A Progressive Fusion of Optimal Features

作     者：Ansam A.Abdulhussien Mohammad F.Nasrudin Saad M.Darwish Zaid A.Alyasseri 

作者机构：Centre of Artificial IntelligenceFaculty of Information Sciences and TechnologyUniversity Kebangsaan MalaysiaBangi43600Malaysia Information Technology CenterIraqi Commission for Computers and InformaticsBaghdad10009Iraq Institute of Graduate Studies and ResearchUniversity of Alexandria163 Horreya AvenueEl Shatby21526P.O.Box 832AlexandriaEgypt 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第75卷第4期

页      面：219-242页

核心收录：

学科分类：0710[理学-生物学] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：CAIT Universiti Kebangsaan Malaysia, UKM 

主　　题：Offline signature verification biometric system feature fusion one-class classifier 

摘      要：Signature verification is regarded as the most beneficial behavioral characteristic-based biometric feature in security and fraud *** is also a popular biometric authentication technology in forensic and commercial transactions due to its various advantages,including noninvasiveness,user-friendliness,and social and legal *** to the literature,extensive research has been conducted on signature verification systems in a variety of languages,including English,Hindi,Bangla,and ***,the Arabic Offline Signature Verification(OSV)system is still a challenging issue that has not been investigated as much by researchers due to the Arabic script being distinguished by changing letter shapes,diacritics,ligatures,and overlapping,making verification more ***,signature verification systems have shown promising results for recognizing signatures that are genuine or forgeries;however,performance on skilled forgery detection is still *** existing methods require many learning samples to improve verification accuracy,which is a major drawback because the number of available signature samples is often limited in the practical application of signature verification *** study addresses these issues by presenting an OSV system based on multifeature fusion and discriminant feature selection using a genetic algorithm(GA).In contrast to existing methods,which use multiclass learning approaches,this study uses a oneclass learning strategy to address imbalanced signature data in the practical application of a signature verification *** proposed approach is tested on three signature databases(SID)-Arabic handwriting signatures,CEDAR(Center of Excellence for Document Analysis and Recognition),and UTSIG(University of Tehran Persian Signature),and experimental results show that the proposed system outperforms existing systems in terms of reducing the False Acceptance Rate(FAR),False Rejection Rate(FRR),and Equa