Wavelet-Based Off-Line Handwritten Signature Verification

Deng, Peter Shaohua; Liao, Hong-Yuan Mark; Ho, Chin Wen; Tyan, Hsiao-Rong

doi:10.1006/cviu.1999.0799

Cited by 88 publications

(32 citation statements)

References 23 publications

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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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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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“…Deng [33] developed a system that used a closed contour tracing algorithm to represent the edges of each signature with several closed contours. The curvature data of the traced closed contours were decomposed into multi-resolution signals using wavelet transforms.…”

Section: 3off-line Signature Recognitionmentioning

confidence: 99%

Off-Line Signature Recognition Systems

Kekre¹,

Bharadi²

2010

IJCA

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Handwritten signature is one of the most widely used biometric traits for authentication of person as well as document. In this paper we discuss issues regarding off-line signature recognitions. We review existing techniques, their performance and method for feature extraction. We discuss a system designed using cluster based global features which is a multi algorithmic offline signature recognition system.

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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%

“…This is called -impostor validation‖ and can be achieved through strategies like test normalization (see [26]). These techniques enable one to construct verifiers that detect random forgeries very accurately (see [7,8]). Since we aim to detect only skilled and casual forgeries, and since models for these forgeries are generally unobtainable, we are not able to utilise any of these impostor validation techniques.…”

Section: Verificationmentioning

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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Wavelet-Based Off-Line Handwritten Signature Verification

Cited by 88 publications

References 23 publications

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

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

Off-Line Signature Recognition Systems

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

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