Off-line signature verification by the tracking of feature and stroke positions

Fang, Bin; Leung, C. H.; Tang, Yuan Yan; Tse, K. W.; Kwok, P.C.K.; Wong, Y.K.

doi:10.1016/s0031-3203(02)00061-4

Cited by 133 publications

(57 citation statements)

References 15 publications

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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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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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“…Feature Extraction: Features for static signature verification can be one of three types [16,17]: (i) global: extracted from every pixel that lie within a rectan- gle circumscribing the signature, including image gradient analysis [18] , series expansions [19], etc., (ii) statistical: derived from the distribution of pixels of a signature, e.g., statistics of high gray-level pixels to identify pseudo-dynamic characteristics [20], (iii) geometrical and topological: e.g., local correspondence of stroke segments to trace signatures [21], feature tracks and stroke positions [16], etc. A combination of all three types of features were used in a writer independent (WI) signature verification system [13,22].…”

Section: Signature Test-bedmentioning

confidence: 99%

Biometric and Forensic Aspects of Digital Document Processing

Srihari

Huang

Srinivasan

et al. 2007

Digital Document Processing

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Abstract. Signatures and handwriting have long played a role in dayto-day business transactions and in forensics, e.g., to authenticate documents, as evidence to establish crime or innocence, etc. The individuality of handwriting and signatures is the basis for their relevance to authentication and forensics. This very individuality makes them also potentially useful as a biometric modality. This chapter is concerned with automatic methods for verifying the writership of handwritten documents and signatures. The discussion consists of the individuality of handwriting, image pre-processing and interactive tools for forensic document examination, discriminating characteristics of handwriting, a statistical model of writer verification, and application of the model to signature verification.

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“…These features we have to calculate for every signatrure image in both training and testing. Now total twelve feature points (v 1 , ..., v 6 and h 1 , ..., h 6 ) are calculated by vertical and horizontal splittings. In Section 4 we will see how each feature point can classify.…”

Section: Feature Points Based On Horizontal Splittingmentioning

confidence: 99%

“…Pattern points based on horizontal splitting are shown below. 6 , h 2,6 , ..., h n, 6 ) 6 are horizontal splitting features of i th training signature sample. Threshold based on horizontal splitting is shown below.…”

Section: Trainingmentioning

confidence: 99%

“…A lot of work has been done in the field of automatic off-line signature verification. While a large portion of work is focused on random forgery detection, more efforts are still needed to address the problem of skilled forgery detection [6]. Our method will be the first verification system which seperates some skilled forgeries from originals.…”

Section: Introductionmentioning

confidence: 99%

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Novel Features for Off-line Signature Verification

Majhi

Reddy

Babu

2006

INT J COMPUT COMMUN

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Abstract:In this paper a novel feature extraction scheme has been suggested for offline signature verification. The proposed method used geometric center for feature extraction. Euclidean distance model was used for classification. This classifier is well suitable for features extracted and fast in computation. Method proposed in this paper leads to better results than existing offline signature verification methods. Threshold selection is based on statistical parameters like average and standard deviation (σ ).

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Off-line signature verification by the tracking of feature and stroke positions

Cited by 133 publications

References 15 publications

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

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

Biometric and Forensic Aspects of Digital Document Processing

Novel Features for Off-line Signature Verification

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