Neural Network-based Handwritten Signature Verification

McCabe, Alan; Trevathan, Jarrod; Read, Wayne

doi:10.4304/jcp.3.8.9-22

Cited by 69 publications

(53 citation statements)

References 17 publications

(14 reference statements)

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“…Alan McCabe et al [25] proposed a method for verifying handwritten signatures by using NN architecture. Various static (e.g., height, slant, etc.)…”

Section: 2neural Network Approachmentioning

confidence: 99%

A Preliminary Study on Various Off-line Hand Written Signature Verification Approaches

Arya¹,

Inamdar²

2010

IJCA

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Biometrics can be classified into two broad categoriesbehavioral (signature verification, keystroke dynamics, etc.) and physiological (iris characteristics, fingerprint, etc.). Handwritten signature is amongst the first few biometrics to be used even before the advent of computers. Signature verification is widely studied and discussed using two approaches [5]. On-line approach uses an electronic tablet and a stylus connected to a computer to extract information about a signature and takes dynamic information like; pressure, velocity, etc whereas in offline approach stable dynamic variations are not used for verification purpose. Offline systems are more applicable and easy to use in comparison with on-line systems in many parts of the world however it is considered more difficult than on-line verification due to the lack of dynamic information. The paper presents a survey of off-line signature verification approaches being followed in different areas. This being a nascent area under research, the survey covers some of the examples of the ways

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“…Alan McCabe et al [25] proposed a method for verifying handwritten signatures by using NN architecture. Various static (e.g., height, slant, etc.)…”

Section: 2neural Network Approachmentioning

confidence: 99%

A Preliminary Study on Various Off-line Hand Written Signature Verification Approaches

Arya¹,

Inamdar²

2010

IJCA

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“…A modern mathematical tool for the analysis of spectral characteristics of nonstationary signals is wavelet analysis. Some well-known studies where wavelet transform was applied to calculate attributes using signatures have been noted [11][12][13][14][15]. The present paper proposes a transition from the time-domain representation of the functions of the pen position change to the frequency-domain representation, their research, and a search of dynamical characteristics using a method of multiresolution analysis.…”

Section: Daubechies Wavelet Transform Coefficientsmentioning

confidence: 99%

Methods of Generating Key Sequences Based on Parameters of Handwritten Passwords and Signatures

et al. 2016

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Abstract:The modern encryption methods are reliable if strong keys (passwords) are used, but the human factor issue cannot be solved by cryptographic methods. The best variant is binding all authenticators (passwords, encryption keys, and others) to the identities. When a user is authenticated by biometrical characteristics, the problem of protecting a biometrical template stored on a remote server becomes a concern. The paper proposes several methods of generating keys (passwords) by means of the fuzzy extractors method based on signature parameters without storing templates in an open way.

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“…В настоящее время предпочтение все чаще отда-ется нейросетевому подходу, и это можно объяснить рядом причин [2]. Во-первых, за последние полвека развитие нейросетевых технологий сделало большой скачок.…”

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Neural network approach to verifying manual signature

Глущенко¹,

Коннова²

2018

PTSJ

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Введение. Одно из самых распространенных направлений в развитии нейросе-тевых технологий связано с биометрией -технологией, посвященной измере-нию и анализу различных физических характеристик человека. Здесь преду-смотрены автоматизированные меры верификации (сравнение образца с био-метрическим шаблоном) и идентификации (сравнение образца со многими из базы данных) личности, основанные на физиологических (например, отпечатки пальцев, радужная оболочка глаза, сетчатка глаза, форма лица) и поведенческих (например, речь, почерк, походка человека и т. д.) принципах. Верификация рукописной подписи является одним из наиболее часто ис-пользуемых методов, например, при проведении финансовых и коммерческих транзакций, проверке документов и контроле физического доступа.Существует два подхода к верификации подписи, различающихся по спосо-бу получения данных:-статический (офлайн) метод, заключающийся в обработке и анализе изоб-ражения подписи; -динамический (онлайн) метод, основанный на считывании подписи в ре-жиме реального времени и выделении таких характеристик, как скорость пись-ма, угол наклона пера, его давления и т. п.

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Neural Network-based Handwritten Signature Verification

Cited by 69 publications

References 17 publications

A Preliminary Study on Various Off-line Hand Written Signature Verification Approaches

A Preliminary Study on Various Off-line Hand Written Signature Verification Approaches

Methods of Generating Key Sequences Based on Parameters of Handwritten Passwords and Signatures

Neural network approach to verifying manual signature

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