Off-line signature recognition using parameterized Hough transform

Kaewkongka, Tonphong; Chamnongthai, Kosin; Thipakorn, Bundit

doi:10.1109/isspa.1999.818209

Cited by 26 publications

(11 citation statements)

References 7 publications

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“…Hough transform is proposed for signature recognition by using Hough space parameter as a global feature [30]. The purpose of Hough transform is to find imperfect instances of objects within a certain class of shapes by a voting procedure.…”

Section: Hough Transformmentioning

confidence: 99%

Offline Handwritten Signature based Blind Biometric Watermarking and Authetication Technique using Biorthogonal Wavelet Transform

Inamdar¹,

Rege²,

Arya³

2010

IJCA

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A method for establishing the identity of an individual is essential in all transactions whether they are commercial or personal. The ability to establish identity with certainty can prevent fraud or forgery. In the midst of an electronic revolution, this remains a major concern in ecommerce, telecommunications, healthcare, and security. In this paper, we present a novel method for biometric image watermarking using the biorthogonal wavelet transform and authentication of the recovered signature from the image data. In proposed approach the offline signature, which is a biometric characteristics of owner is embedded in second level detailed coefficients of discrete wavelet transform of cover image. The novelty of the proposed scheme is that, it also goes a step further wherein it extracts the features of recovered signatures and does the template matching with features of signature data base. GENERAL TERMSInformation hiding, Digital Right Managements

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Section: Hough Transformmentioning

confidence: 99%

Offline Handwritten Signature based Blind Biometric Watermarking and Authetication Technique using Biorthogonal Wavelet Transform

Inamdar¹,

Rege²,

Arya³

2010

IJCA

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“…Kaewkongka, Chamnongthai and Thipakom [45] proposed a method of an off-line signature recognition by using Hough transform to detect stroke lines from signature image. The Hough transform was used to extract the parameterized Hough space from signature skeleton as unique characteristic feature of signatures.…”

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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Off-line signature recognition using parameterized Hough transform

Cited by 26 publications

References 7 publications

Offline Handwritten Signature based Blind Biometric Watermarking and Authetication Technique using Biorthogonal Wavelet Transform

Offline Handwritten Signature based Blind Biometric Watermarking and Authetication Technique using Biorthogonal Wavelet Transform

Off-Line Signature Recognition Systems

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

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