Verification of dynamic signature using machine learning approach

Chandra, Subhash

doi:10.1007/s00521-019-04669-w

Cited by 16 publications

(8 citation statements)

References 21 publications

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“…The falsely rejected rate (FRR) and falsely accepted rate (FAR) show an improvised result in our proposed approach as compared with others. The FRR value shows a significant decline of 5.4421, 5.7021 and 2.5611 while comparing with [27], [17] and [26].…”

Section: Experiments Performance Resultsmentioning

confidence: 88%

“…The FRR and FAR of our suggested system based on the SVC2004 databases are shown in table VII. The experimental results are based on three parameters: falsely rejected rate (FRR), falsely accepted rate (FAR), and average mean The proposed approach is compared with [27], [17] and [26] on same dataset. The falsely rejected rate (FRR) and falsely accepted rate (FAR) show an improvised result in our proposed approach as compared with others.…”

Section: Experiments Performance Resultsmentioning

confidence: 99%

“…The performance evaluation [4], [26] of the proposed scheme is measured and analyzed with respect to various evaluation metrics shown and indicated in table III.…”

Section: B Performance Measuresmentioning

confidence: 99%

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Online Signature Classification Based on Dynamic Nature of Features Selection Framework

Kumar Singh,

Kesarwani,

Anushree

et al. 2024

IJCDS

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In the recent digital age, online signature verification plays a crucial role in authentication, including security standards across many industries, such as financial, legal, and e-commerce. Bank'srld Bank's data shows the global digital economy is growing fast, with internet usage for nearly 60% of people worldwide. According to numbers from the International Telecommunications Union, over 4.7 billion individuals have become internet users. With so much internet online, security and trust for online transactions are essential issues. Forensics and biometrics are emerging as key players in this area. Verifying signatures digitally is one important use. As in the study mentioned earlier, using machine learning can help make signature verification systems more accurate and reliable. Our study describes an online verification method using machine learning based on a signature's dynamic features and compares the outcomes to methods already in use. The online signature verification has been validated using supervised learning (K-nearest neighbor (KNN)). This research aimed to enhance authenticity and reduce the occurrence of false positives as its primary objectives. The outcomes show that this methodology has better authenticity than the current methods. The Signature Verification System (SVS) 2004-based signature datasets are utilized in the tests.

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Section: Experiments Performance Resultsmentioning

confidence: 88%

Section: Experiments Performance Resultsmentioning

confidence: 99%

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Online Signature Classification Based on Dynamic Nature of Features Selection Framework

Kumar Singh,

Kesarwani,

Anushree

et al. 2024

IJCDS

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“…In [27], a new approach for online signature verification based on machine learning method is presented. In the method they proposed, they considered the average values of the attributes for validation.…”

Section: Related Workmentioning

confidence: 99%

Signature Verification Using Siamese Neural Network One-Shot Learning

Arısoy

2021

International Journal of Engineering and Innovative Research

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With the acceleration of digitalization in all areas of our lives, the need for biometric verification methods is increasing. The fact that biometric data is unique and biometric verification is stronger against phishing attacks compared to password-based authentication methods, has increased its preference rate. Signature verification, which is one of the biometric verification types, plays an important role in many areas such as banking systems, administrative and judicial applications. There are 2 types of signature verification, online and offline, for identifying the identity of the person and detecting signature forgery. Online signature verification is carried out during signing and temporal dynamic data are available regarding the person's signature. Offline verification is applied by scanning the image after signing, and this verification is limited to spatial data. Therefore, the offline signature verification process is considered a more challenging task.In this study, offline signature verification, independent of the writer, based on One-Shot Learning, was performed using Siamese Neural Network. Due to the fact that the Deep Convolution Neural Network requires a large amount of labeled data for image classification, real and fake signature distinction has been achieved by using the One-Shot Learning method, which can perform a successful classification by using less numbers of signature images. As a result of the experiments conducted on signature datasets, using the Siamese architecture, the proposed approach achieved percentage accuracy of 93.23, 90.11, 89.99, 92.35 verification in 4NSigComp2012, SigComp2011, 4NSigComp2010 and BHsig260 respectively.

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“…In this experiment, the proposed NRVS model is evaluated by comparing the results of it with (i) six well-known learning algorithms, namely, Multilayer Perceptron (MLP) [47], Support Vector Machines (SVM) [48], Linear Discriminant Analysis (LDA) [49], Decision Tree (DT) [50], Naive Bayes (NB) [51], and Random Forest (RF) [51] classifiers, and (ii) one of the most related methods such as the proposed one in [46]. The method in [46] was based on getting the most insightful features.…”

Section: Nrvs Vs Conventional Classifiersmentioning

confidence: 99%

Neutrosophic Rule-Based Identity Verification System Based on Handwritten Dynamic Signature Analysis

Hefny¹,

Hassanien²,

Basha³

2021

Computers, Materials &Amp; Continua

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Identity verification using authenticity evaluation of handwritten signatures is an important issue. There have been several approaches for the verification of signatures using dynamics of the signing process. Most of these approaches extract only global characteristics. With 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) models. The neutrosophic Logic is structured to reflect multiple types of knowledge and relations among all features using three values: truth, indeterminacy, and falsity. These three values are determined by neutrosophic membership functions. The proposed model also is able to deal with all features without the need to select from them. In the GNRVS model, the neutrosophic rules are automatically chosen by Genetic Algorithms. The performance of the proposed system is tested on the MCYT-Signature-100 dataset. In 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.

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Verification of dynamic signature using machine learning approach

Cited by 16 publications

References 21 publications

Online Signature Classification Based on Dynamic Nature of Features Selection Framework

Online Signature Classification Based on Dynamic Nature of Features Selection Framework

Signature Verification Using Siamese Neural Network One-Shot Learning

Neutrosophic Rule-Based Identity Verification System Based on Handwritten Dynamic Signature Analysis

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