Offline Signature Verification Using Online Handwriting Registration

Qiao, Yu; Liu, Jianzhuang; Tang, Xiaoou

doi:10.1109/cvpr.2007.383263

Cited by 37 publications

(26 citation statements)

References 23 publications

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“…In particular, we will focus on past research which addresses the direct comparison of on-line and off-line signature verification performance and the feasibility of combining them in order to improve their overall recognition accuracy. Accordingly, other works that may be found in the literature which exploit certain common features between dynamic and static samples with different goals such as improving off-line signature segmentation [14], or aiding off-line signature recognition based on previous on-line enrolment [15], will not be covered here.…”

Section: Related Workmentioning

confidence: 99%

On-line signature recognition through the combination of real dynamic data and synthetically generated static data

Galbally

Diaz

Ferrer

et al. 2015

Pattern Recognition

107

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This is the author’s version of a work that was accepted for publication in Pattern Recognition . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition , 48, 9 (2005) DOI: 10.1016/j.patcog.2015.03.019On-line signature verification still remains a challenging task within biometrics. Due to their behavioral nature (opposed to anatomic biometric traits), signatures present a notable variability even between successive realizations. This leads to higher error rates than other largely used modalities such as iris or fingerprints and is one of the main reasons for the relatively slow deployment of this technology. As a step towards the improvement of signature recognition accuracy, the present paper explores and evaluates a novel approach that takes advantage of the performance boost that can be reached through the fusion of on-line and off-line signatures. In order to exploit the complementarity of the two modalities, we propose a method for the generation of enhanced synthetic static samples from on-line data. Such synthetic off-line signatures are used on a new on-line signature recognition architecture based on the combination of both types of data: real on-line samples and artificial off-line signatures synthesized from the real data. The new on-line recognition approach is evaluated on a public benchmark containing both real versions (on-line and off-line) of the exact same signatures. Different findings and conclusions are drawn regarding the discriminative power of on-line and off-line signatures and of their potential combination both in the random and skilled impostors scenarios.M. D.-C. is supported by a PhD fellowship from the ULPGC and M.G.-B. is supported by a FPU fellowship from the Spanish MECD. This work has been partially supported by projects: MCINN TEC2012-38630- C04-02, Bio-Shield (TEC2012-34881) from Spanish MINECO, BEAT (FP7-SEC-284989) from EU, CECABANK and Cátedra UAM-Telefónic

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Section: Related Workmentioning

confidence: 99%

On-line signature recognition through the combination of real dynamic data and synthetically generated static data

Galbally

Diaz

Ferrer

et al. 2015

Pattern Recognition

107

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“…The global features can deliver limited information for signature verification [14]. Small distortions in isolated regions of the signature do not cause a major impact on the global feature vector.…”

Section: Choice Of Featuresmentioning

confidence: 99%

“…On the other hand, local features provide rich descriptions of writing shapes and are powerful for discriminating writers, but the extraction of reliable local features is still a hard problem [14].…”

Section: Choice Of Featuresmentioning

confidence: 99%

“…Local features are applied to the cells of a grid virtually super imposed on a signature image or to particular elements obtained after signature segmentation. These features are calculated to describe the geometrical and topological characteristics of local segments, such as position, tangent direction, and curvature [14]. They represent a segment or limited region of the signature image, such as critical junctions and gradients.…”

Section: Local Features -Local Features Are Extracted Frommentioning

confidence: 99%

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Approaches and Issues in Offline Signature Verification System

Saikia¹,

Sarma²

2012

IJCA

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“…Some efforts have been performed through off-line signature verification using simple methods to create static signatures [8], [9]. In this paper, we propose different approaches to generate synthetic enhanced static data that performs similarly, or even outperforms, real off-line samples, taking into account dynamic features during the synthesis process.…”

Section: Introductionmentioning

confidence: 99%

Generation of Enhanced Synthetic Off-Line Signatures Based on Real On-Line Data

Diaz

Gomez‐Barrero

Morales

et al. 2014

2014 14th International Conference on Frontiers in Handwriting Recognition

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: Abstract-One of the main challenges of off-line signature verification is the absence of large databases. A possible alternative to overcome this problem is the generation of fully synthetic signature databases, not subject to legal or privacy concerns. In this paper we propose several approaches to the synthesis of off-line enhanced signatures from real dynamic information. These synthetic samples show a performance very similar to the one offered by real signatures, even increasing their discriminative power under the skilled forgeries scenario, one of the biggest challenges of handwriting recognition. Furthermore, the feasibility of synthetically increasing the enrolment sets is analysed, showing promising results.

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Offline Signature Verification Using Online Handwriting Registration

Abstract: Abstract

Cited by 37 publications

References 23 publications

On-line signature recognition through the combination of real dynamic data and synthetically generated static data

On-line signature recognition through the combination of real dynamic data and synthetically generated static data

Approaches and Issues in Offline Signature Verification System

Generation of Enhanced Synthetic Off-Line Signatures Based on Real On-Line Data

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