Using adapted Levenshtein distance for on-line signature authentication

Schimke, Sascha; Vielhauer, Claus; Dittmann, Jana

doi:10.1109/icpr.2004.1334412

Cited by 41 publications

(23 citation statements)

References 5 publications

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“…The idea to use LD for biometrics is not new, e.g. Schimke et al [12] employ an adapted version of LD based on event-string modelling and a nearest neighbor classifier for online signature verification. In this work, we assess the usability of LD for iris recognition in order to cope with imperfect normalization of iris textures.…”

Section: Levenshtein Distance In Iris Recognitionmentioning

confidence: 99%

Enhancing Iris Matching Using Levenshtein Distance with Alignment Constraints

Uhl

Wild

2010

Advances in Visual Computing

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Abstract. Iris recognition from surveillance-type imagery is an active research topic in biometrics. However, iris identification in unconstrained conditions raises many proplems related to localization and alignment, and typically leads to degraded recognition rates. While development has mainly focused on more robust preprocessing, this work highlights the possibility to account for distortions at matching stage. We propose a constrained version of the Levenshtein Distance (LD) for matching of binary iris-codes as an alternative to the widely accepted Hamming Distance (HD) to account for iris texture distortions by e.g. segmentation errors or pupil dilation. Constrained LD will be shown to outperform HD-based matching on CASIA (third version) and ICE (2005 edition) datasets. By introducing LD alignment constraints, the matching problem can be solved in O(n · s) time and O(n + s) space with n and s being the number of bits and shifts, respectively.

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Section: Levenshtein Distance In Iris Recognitionmentioning

confidence: 99%

Enhancing Iris Matching Using Levenshtein Distance with Alignment Constraints

Uhl

Wild

2010

Advances in Visual Computing

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“…This distance measure deter-621 mines a value for the similarity of two character strings. To get one of these charac-622 ter strings, the online signature sample data must be transferred into a sequence of 623 characters as described by Schimke et al [30]: from the handwriting raw data (pen 624 position and pressure), the pen movement can be interpolated and other signals can 625 be determined, such as the velocity. The local extrema (minimum, maximum) of the 626 function curves of the pen movement are used to transfer a signature into a string.…”

Section: Reference System 2 (Ref2 V10)mentioning

confidence: 99%

“…This problem of simultaneous events can be treated by the creation of a 643 combined event, requiring the definition of scores for edit operations on those com-644 bination events. In this approach, an additional normalization of the distance is per-645 formed due to the possibility of different lengths of the two string sequences [30]. 646 This is necessary because the lengths of the strings created using the pen signals 647 can be different due to the fluctuations of the biometric input.…”

Section: Reference System 2 (Ref2 V10)mentioning

confidence: 99%

“…Online signature verification thus differs from 25 off-line signature verification by the nature of the raw signal that is captured: off- 26 line signature verification processes the signature as an image, digitized by means 27 of a scanner [25,24,8] while online signature is captured through an appropri- 28 ate sensor sampling at regular time intervals the hand-drawn signal. Such sensors 29 have evolved recently allowing to capture on each point not only pen position but 30 also pen pressure and pen inclination in a three-dimensional space. interfaces such as those on Personal Digital Assistants (PDAs) and Smartphones 32 operate via a touch screen to allow only a handwritten signature as a time sequence 33 of pen coordinates to be captured.…”

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Online Handwritten Signature Verification

Garcia-Salicetti

Houmani

Ly-Van

et al. 2009

Guide to Biometric Reference Systems and Performance Evaluation

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“…Coyette et al use a method that is also used in biometric characteristics recognition [115] and is based on comparison between string vectors. More specifically, each hand drawn shape is translated into a sequence of line segments.…”

Section: Flexisketchmentioning

confidence: 99%

Type Inference in Flexible Model-Driven Engineering

Zolotas

Matragkas

Devlin

et al. 2015

Lecture Notes in Computer Science

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Model-driven Engineering (MDE) is an approach to software development that promises increased productivity and product quality. Domain models that conform to metamodels, both of which are the core artefacts in MDE approaches, are manipulated to perform different development processes using specific MDE tools.However, domain experts, who have detailed domain knowledge, typically lack the technical expertise to transfer this knowledge using MDE tools. Flexible or bottom-up Model-driven Engineering is an emerging approach to domain and systems modelling that tackles this challenge by promoting the use of simple drawing tools to increase the involvement of domain experts in MDE processes. In this approach, no metamodel is created upfront but instead the process starts with the definition of example models that will be used to infer a draft metamodel. When complete knowledge of the domain is acquired, a final metamodel is devised and a transition to traditional MDE approaches is possible. However, the lack of a metamodel that encodes the semantics of conforming models and of tools that impose these semantics bears some drawbacks, among others that of having models with nodes that are unintentionally left untyped. In this thesis we propose the use of approaches that use algorithms from three different research areas, that of classification algorithms, constraint programming and graph similarity to help with the type inference of such untyped nodes. We perform an evaluation of the proposed approaches in a number of randomly generated example models from 10 different domains with results suggesting that the approaches could be used for type inference both in an automatic or a semi-automatic style. 3For my parents Despoina and Michalis

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Using adapted Levenshtein distance for on-line signature authentication

Abstract: Abstract

Cited by 41 publications

References 5 publications

Enhancing Iris Matching Using Levenshtein Distance with Alignment Constraints

Enhancing Iris Matching Using Levenshtein Distance with Alignment Constraints

Online Handwritten Signature Verification

Type Inference in Flexible Model-Driven Engineering

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