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Meenakshi K. Kalera

Bio: Meenakshi K. Kalera is an academic researcher from University at Buffalo. The author has contributed to research in topics: Handwriting & Feature extraction. The author has an hindex of 1, co-authored 1 publications receiving 303 citations.

Papers
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Journal ArticleDOI
TL;DR: This paper describes a novel approach for signature verification and identification in an offline environment based on a quasi-multiresolution technique using GSC (Gradient, Structural and Concavity) features for feature extraction using a mapping from the handwriting domain to the signature domain.
Abstract: This paper describes a novel approach for signature verification and identification in an offline environment based on a quasi-multiresolution technique using GSC (Gradient, Structural and Concavity) features for feature extraction. These features when used at the word level, instead of the character level, yield promising results with accuracies as high as 78% and 93% for verification and identification, respectively. This method was successfully employed in our previous theory of individuality of handwriting developed at CEDAR — based on obtaining within and between writer statistical distance distributions. In this paper, exploring signature verification and identification as offline handwriting verification and identification tasks respectively, we depict a mapping from the handwriting domain to the signature domain.

343 citations


Cited by
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Journal ArticleDOI
01 Sep 2008
TL;DR: This paper presents the state of the art in automatic signature verification and addresses the most valuable results obtained so far and highlights the most profitable directions of research to date.
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.

688 citations

Journal ArticleDOI
TL;DR: A novel formulation of the problem that includes knowledge of skilled forgeries from a subset of users in the feature learning process, that aims to capture visual cues that distinguish genuine signatures and forgeries regardless of the user is proposed.

252 citations

Journal ArticleDOI
TL;DR: This paper proposes the use of One-Class Support Vector Machine (OC-SVM) based on writer-independent parameters, which takes into consideration only genuine signatures and when forgery signatures are lack as counterexamples for designing the HSVS.

197 citations

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TL;DR: The proposed feature set describes the shape of a signature in terms of spatial distribution of black pixels around a candidate pixel (on the signature) and provides a measure of texture through the correlation among signature pixels in the neighborhood of that candidate pixel.

189 citations

Journal ArticleDOI
TL;DR: A systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario is reported, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject.
Abstract: Handwritten signatures are biometric traits at the center of debate in the scientific community. Over the last 40 years, the interest in signature studies has grown steadily, having as its main reference the application of automatic signature verification, as previously published reviews in 1989, 2000, and 2008 bear witness. Ever since, and over the last 10 years, the application of handwritten signature technology has strongly evolved and much research has focused on the possibility of applying systems based on handwritten signature analysis and processing to a multitude of new fields. After several years of haphazard growth of this research area, it is time to assess its current developments for their applicability in order to draw a structured way forward. This perspective reports a systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject.

184 citations