Online signature verification based on writer dependent features and classifiers

Manjunatha, K.; Manjunath, S.; Guru, D. S.; Somashekara, M.T.

doi:10.1016/j.patrec.2016.06.016

Cited by 58 publications

(16 citation statements)

References 35 publications

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“…Their method is based on efficient encoding of the geometric structure of signatures with grid templates that are properly partitioned into subsets. In [19], they developed an online signature verification approach based on writer-specific features, and an again on writer-specific classifier. Which features would best suit the author and which classifier would be used to verify the author were taken according to the error rate obtained with the training samples.…”

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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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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“…Pages: 33 -40 of genuine signatures of the user needs to be collected. Later, when users provide a signature which they claim to be of a particular individual, the system trains a writer dependent classifier [2] using those images which will be used to classify signatures for that particular user. A certain threshold value can be assumed on the basis of confidence required for signature verification.…”

Section: Similar To Other Biometric Verification Systems Imagesmentioning

confidence: 99%

Signature Verification using Convolutional Neural Network and Autoencoder

Prajapati¹,

Poudel²,

Baduwal³

et al. 2021

J. Inst. Engineering

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Signature has been one of the widely used verification biometrics out there. Handwritten signatures are used in cheques, forms, letters, applications, minutes, etc. The Signature of every individual is unique in nature, that is why it is essential that a person’s handwritten signature be uniquely identified. Signature Verification is a widely used method for authenticating any individual during absence. Human verification is prone to inaccuracy and sometimes indecisiveness. This paper presents an investigation of using Convolutional Neural Network (CNN) for Writer-Dependent models in signature verification. Random distortions were generated in genuine images using an autoencoder to get forged signatures, which were passed to the classifier during training. The paper details all the pre-processing steps carried out on the image and shows various test results for changing the number of training sets of images. The average test accuracy for Persian dataset is 83% when the system was trained with 22 genuine images. There was a decrease of 9.4% in accuracy when the model was trained with 9 genuine images.

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“…For offline signatures, we can find either writer-dependent proposals [165], which build a mathematical model per writer, or writer-independent proposals [112], which develop a unique model in the ASV. One was a set of global features based on the boundary of a signature, such as its total energy, the vertical and horizontal projections, and the overall box size in which the signature is contained [181].…”

Section: Western Signature Verification Systemsmentioning

confidence: 99%

A Perspective Analysis of Handwritten Signature Technology

Diaz¹,

et al. 2019

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Handwritten signatures are biometric traits at the center of debate in the scientific community. Over the last 40 years, the interest in signature studies has grown steadily, having as its main reference the application of automatic signature verification, as previously published reviews in 1989, 2000, and 2008 bear witness. Ever since, and over the last 10 years, the application of handwritten signature technology has strongly evolved and much research has focused on the possibility of applying systems based on handwritten signature analysis and processing to a multitude of new fields. After several years of haphazard growth of this research area, it is time to assess its current developments for their applicability in order to draw a structured way forward. This perspective reports a systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject. CCS Concepts: • General and reference → Surveys and overviews; • Security and privacy → Biometrics; Access control; • Theory of computation → Pattern matching; • Applied computing → Document analysis; System forensics;

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Online signature verification based on writer dependent features and classifiers

Cited by 58 publications

References 35 publications

Signature Verification Using Siamese Neural Network One-Shot Learning

Signature Verification Using Siamese Neural Network One-Shot Learning

Signature Verification using Convolutional Neural Network and Autoencoder

A Perspective Analysis of Handwritten Signature Technology

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