A Hybrid Statistical Modelling, Normalization and Inferencing Techniques of an Off-Line Signature Verification System

Ahmad, Sharifah Mumtazah Syed; Shakil, Asma; Faudzi, Masyura Ahmad; Anwar, Rina Md.; Balbed, Mustafa Agil Muhamad

doi:10.1109/csie.2009.973

Cited by 10 publications

(3 citation statements)

References 6 publications

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“…Sharifah Mumtazah Syed Ahmad et al [21] presented an automatic off-line signature verification system built with several statistical techniques. They used Hidden Markov Modeling (HMM) technique to build a reference model for each local feature.…”

Section: Some Approaches In Offline Signature Verificationmentioning

confidence: 99%

Approaches and Issues in Offline Signature Verification System

Saikia¹,

Sarma²

2012

IJCA

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Section: Some Approaches In Offline Signature Verificationmentioning

confidence: 99%

Approaches and Issues in Offline Signature Verification System

Saikia¹,

Sarma²

2012

IJCA

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“…An automatic off-line signature verification system presented in [5] is built with several statistical techniques. They used Hidden Markov Modeling (HMM) technique to build a reference model for each local feature.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Offline Signature Verification System

Arathi¹,

Govardhan²

2014

IJMLC

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Abstract-Offline signature verification is one of most challenging area of pattern recognition. Many methods have been introduced in literature to find whether a given signature is genuine or forgery. In the proposed work, the signature image is converted into time series data using linear scanning method and then time series shapelets are identified to distinguish genuine signatures from forged ones. The shapelets are time series subsequences which are maximally representative of a class. To compare the time series data, the proposed method uses Mahalanobis distance measure. The experimental results show that the method has great reduction in equal error rate.

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“…Ali Karouni et.al [12] has developed new method using artificial neural network. Sharifah Mumtazah Syed Ahmad et.al [13] presented an automatic off-line signature verification system using Hidden Markov Modeling (HMM). Vu Nguyen et.al [14] used the total energy that a writer uses to create his/her signature as a global feature and combined these features with the Modified Direction Feature (MDF) and SVM were employed to construct the signature models.…”

Section: Introductionmentioning

confidence: 99%

An Offline Signature Verification using Adaptive Resonance Theory 1 (ART1)

Jain¹,

Singh²,

Chugh³

2014

IJCA

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Automatic signature verification is a well-established and an active area of research with numerous applications such as bank check verification, ATM access, etc. This paper proposes a novel approach to the problem of automatic off-line signature verification and forgery detection. We have designed offline signature verification and recognition system (SVRS) using Adaptive Resonance Theory-1(ART 1). In this paper a standard database of 250 signatures is used for calculating the performance of SVRS. The training of our system is done using ART-1 that uses global features as input vector and the verification and recognition phase uses a two step process. In first step, the input vector is matched with stored reference vector which was used as training set & in second step cluster formation takes place. If our given pattern matches with the stored pattern, it is accepted otherwise new cluster is formed. The presented approach achieved a classification ratio of 97.9% .The false acceptance rate (FAR) and false rejection rate (FRR) for given sample signatures is 2.7% and 3.9%.

show abstract

A Hybrid Statistical Modelling, Normalization and Inferencing Techniques of an Off-Line Signature Verification System

Cited by 10 publications

References 6 publications

Approaches and Issues in Offline Signature Verification System

Approaches and Issues in Offline Signature Verification System

An Efficient Offline Signature Verification System

An Offline Signature Verification using Adaptive Resonance Theory 1 (ART1)

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