Investigating the Common Authorship of Signatures by Off-Line Automatic Signature Verification Without the Use of Reference Signatures

Diaz, Moises; Ferrer, Miguel A.; Ramalingam, Soodamani; Guest, Richard

doi:10.1109/tifs.2019.2924195

Cited by 12 publications

(3 citation statements)

References 62 publications

(108 reference statements)

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“…Examines psychological aspects of handwriting to assess authenticity [56] Machine learning Utilizes supervised learning models for signature verification based on training data [23] Local features Identify keypoints or interest points in signatures for robust feature matching. [47] Stroke sequence analysis Studies the order and sequence of strokes in a signature [50] Local binary patterns Encodes texture information for signature representation and comparison [53] Handwriting feature analysis Analyzes individual handwriting features like slant, size, and pressure [68] Graph-based approaches Represent signatures as graphs and analyze structural information for authentication [45] Forensic document analysis Investigates paper, ink, and other physical aspects of the document [55,69] Signature comparison Compares questioned signatures to known reference signatures [17] Biometric authentication Uses biometric data like pen pressure and speed for verification [19] Neural networks Employs deep learning models for complex signature analysis [43,74] signatures of individuals. Additionally, a transparent UBM, which ought to be understood by itself [40], is used as input.…”

Section: Graphology Analysismentioning

confidence: 99%

Explainable Offline Automatic Signature Verifier to Support Forensic Handwriting Examiners

Diaz¹,

Ferrer²,

Vessio³

2023

SSRN Journal

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Section: Graphology Analysismentioning

confidence: 99%

Explainable Offline Automatic Signature Verifier to Support Forensic Handwriting Examiners

Diaz¹,

Ferrer²,

Vessio³

2023

SSRN Journal

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“…Such methods have achieved superior results in multiple benchmarks. Readers interested in the state of the art of signature verification systems, should please refer to these works and to [28]- [31]. In this section, we cover the core of our work, i.e., signature duplication methods and data augmentation methods in the feature space.…”

Section: Related Workmentioning

confidence: 99%

Intrapersonal Parameter Optimization for Offline Handwritten Signature Augmentation

Maruyama¹,

Oliveira²,

Britto³

et al. 2020

Preprint

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Usually, in a real-world scenario, few signature samples are available to train an automatic signature verification system (ASVS). However, such systems do indeed need a lot of signatures to achieve an acceptable performance. Neuromotor signature duplication methods and feature space augmentation methods may be used to meet the need for an increase in the number of samples. Such techniques manually or empirically define a set of parameters to introduce a degree of writer variability. Therefore, in the present study, a method to automatically model the most common writer variability traits is proposed. The method is used to generate offline signatures in the image and the feature space and train an ASVS. We also introduce an alternative approach to evaluate the quality of samples considering their feature vectors. We evaluated the performance of an ASVS with the generated samples using three well-known offline signature datasets: GPDS, MCYT-75, and CEDAR. In GPDS-300, when the SVM classifier was trained using one genuine signature per writer and the duplicates generated in the image space, the Equal Error Rate (EER) decreased from 5.71% to 1.08%. Under the same conditions, the EER decreased to 1.04% using the feature space augmentation technique. We also verified that the model that generates duplicates in the image space reproduces the most common writer variability traits in the three different datasets.

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“…Automatic signature verification techniques are often divided into two groups [6]. One is an online system, while the other is an offline system [7]. Special equipment, such as a digital pen and a digitizer, is used to collect data in an online system.…”

Section: Introductionmentioning

confidence: 99%

Automatic Signature Verifier Using Gaussian Gated Recurrent Unit Neural Network

Khan,

Singh,

Singh

et al. 2023

IET Biometrics

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Handwritten signatures are one of the most extensively utilized biometrics used for authentication, and forgeries of this behavioral biometric are quite widespread. Biometric databases are also difficult to access for training purposes due to privacy issues. The efficiency of automated authentication systems has been severely harmed as a result of this. Verification of static handwritten signatures with high efficiency remains an open research problem to date. This paper proposes an innovative introselect median filter for preprocessing and a novel Gaussian gated recurrent unit neural network (2GRUNN) as a classifier for designing an automatic verifier for handwritten signatures. The proposed classifier has achieved an FPR of 1.82 and an FNR of 3.03. The efficacy of the proposed method has been compared with the various existing neural network-based verifiers.

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Investigating the Common Authorship of Signatures by Off-Line Automatic Signature Verification Without the Use of Reference Signatures

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 12 publications

References 62 publications

Explainable Offline Automatic Signature Verifier to Support Forensic Handwriting Examiners

Explainable Offline Automatic Signature Verifier to Support Forensic Handwriting Examiners

Intrapersonal Parameter Optimization for Offline Handwritten Signature Augmentation

Automatic Signature Verifier Using Gaussian Gated Recurrent Unit Neural Network

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