Antiforgery: a novel pseudo-outer product based fuzzy neural network driven signature verification system

Quek, Chai; Zhou, Ruqi

doi:10.1016/s0167-8655(02)00153-8

Cited by 47 publications

(25 citation statements)

References 21 publications

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“…), and so on [8], [17], [290]. Other well-known parameters based on slant [8], [17], [59], [270], [301], orientation [290], contour [15], [26], [230], [231], [274], direction [66]- [68], [149], [282], [301], [350], and curvature [138], [145] have also been considered. Conversely, typical parameters extracted at pixel level are grid-based features.…”

Section: Feature Extractionmentioning

confidence: 99%

Automatic Signature Verification: The State of the Art

Impedovo

Pirlo

2008

IEEE Trans. Syst., Man, Cybern. C

540

295

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Abstract-In recent years, along with the extraordinary diffusion of the Internet and a growing need for personal verification in many daily applications, automatic signature verification is being considered with renewed interest. This paper presents the state of the art in automatic signature verification. It addresses the most valuable results obtained so far and highlights the most profitable directions of research to date. It includes a comprehensive bibliography of more than 300 selected references as an aid for researchers working in the field.

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Section: Feature Extractionmentioning

confidence: 99%

Automatic Signature Verification: The State of the Art

Impedovo

Pirlo

2008

IEEE Trans. Syst., Man, Cybern. C

540

295

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“…The same problem is encountered in another online approach employing a neuro-fuzzy method [45], where the best results were obtained for signatures of Chinese individuals.…”

Section: Related Work and Critical Remarksmentioning

confidence: 89%

“…A significant number of contemporary solutions use different kind of neuro-fuzzy applications [18,30,38,45,52]. Data for the fuzzy systems come from extraction of various features like position and pressure [30], angles [38], Zernike moments [18], result of discrete wavelet transform [52] and pseudo-outer product [45].…”

Section: Related Work and Critical Remarksmentioning

confidence: 99%

A new approach to signature recognition using the fuzzy method

Kudłacik

Porwik

2012

Pattern Anal Applic

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The paper presents a new fuzzy approach to off-line handwritten signature recognition. The solution is based on characteristic feature extraction. After finding signature's center of gravity a number of lines are drawn through it at different angles. Cross points of generated lines and signature sample, which are further grouped and sorted, are treated as the set of features. On the basis of such structures, obtained from a chosen number of learning samples, a fuzzy model is created, called the fuzzy signature. During a verification phase the level of conformity of an input sample and the fuzzy signature is calculated. The extension in feature extraction as well as proposed fuzzy model has never been employed before. It needs to be emphasized that information stored within the verification system cannot be used to recreate the original signatures collected at the enrolment phase. The fact is particularly valuable for large databases and systems where storage safety is crucial. The solution is very flexible and allows the user to extend an intuitive structure of fuzzy sets by employing dynamic features, making the approach an on-line method. The results obtained should be still improved, similarly to the case of other known biometric systems related to signature recognition. However, the presented technique can be easily utilized in applications where FAR coefficient should be very low and is more important than FRR ratio.

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“…These techniques include template matching techniques [7,9,11], minimum distance classifiers [10,12,14,15], Neural networks [8,13,16], hidden Markov models (HMMs) [17,18], and structural pattern recognition techniques.…”

Section: Overviewmentioning

confidence: 99%

Invariant features comparison in hidden markov model and SIFT for offline handwritten signature database

Shukla¹,

Sudan²

2010

IJCA

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In Handwritten signatures analyzed for forgery have to undergo feature extraction process, due to varied samples in size rotation and intra-domain changes, invariance has to be achieved during feature extraction process; circular Hidden Markov Model with discrete radon transform approach of feature extraction provides invariance. On other hand Scale Invariant Feature Transform (SIFT) has inherent invariant feature extraction approach. This paper compares both approaches on common signature databases for False acceptance rate(FAR),False Rejection Rate(FRR) and Equal Error Rate(EER) Categories and Subject DescriptorsThe Paper deals in Digital Forensic category where circular Hidden Markov Model(HMM) and Scale invariant Feature Transform(SIFT) invariant features are compared for Offline Handwritten Signature verification General TermsHere we will be dealing with feature extraction of offline handwritten signature with Discrete Radon Transform (DRT) for circular HMM for forgery detection and in second approach scale invariant image features of offline signatures are extracted using SIFT and forgery detection is done.

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Antiforgery: a novel pseudo-outer product based fuzzy neural network driven signature verification system

Cited by 47 publications

References 21 publications

Automatic Signature Verification: The State of the Art

Automatic Signature Verification: The State of the Art

A new approach to signature recognition using the fuzzy method

Invariant features comparison in hidden markov model and SIFT for offline handwritten signature database

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