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Ghost Module Based Residual Mixture of Self-Attention and Convolution for online signature verification

Computers, Materials & Continua 2024年第4期79卷 695-712页

作者： Fangjun Luan Xuewen Mu Shuai Yuan Department of Computer Technology School of Computer Science and EngineeringShenyang Jianzhu UniversityShenyang110168China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Jianzhu UniversityShenyang110168China Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang Jianzhu UniversityShenyang110168China

online signature verification (OSV), as a personal identification technology, is widely used in various ***, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational ***, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

关键词： online signature verification feature selection ACG block ghost-ACmix residual structure

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Neutrosophic Rule-Based Identity verification System Based on Handwritten Dynamic signature Analysis

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Computers, Materials & Continua 2021年第11期69卷 2367-2385页

作者： Amr Hefny Aboul Ella Hassanien Sameh H.Basha Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt Faculty of Computers and Information Cairo UniversityGiza12613Egypt

Identity verification using authenticity evaluation of handwritten signatures is an important *** have been several approaches for the verification of signatures using dynamics of the signing *** of these approaches extract only global *** the aim of capturing both dynamic global and local features,this paper introduces a novel model for verifying handwritten dynamic signatures using neutrosophic rule-based verification system(NRVS)and Genetic NRVS(GNRVS)*** neutrosophic Logic is structured to reflect multiple types of knowledge and relations among all features using three values:truth,indeterminacy,and *** three values are determined by neutrosophic membership *** proposed model also is able to deal with all features without the need to select from *** the GNRVS model,the neutrosophic rules are automatically chosen by Genetic *** performance of the proposed system is tested on the MCYT-signature-100 *** terms of the accuracy,average error rate,false acceptance rate,and false rejection rate,the experimental results indicate that the proposed model has a significant advantage compared to different well-known models.

关键词： Biometrics online signature verification neutrosophic rule-based verification system

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