BioSecure signature evaluation campaign (BSEC'2009): Evaluating online signature algorithms depending on the quality of signatures

Houmani, Nesma; Mayoue, Aurélien; Garcia-Salicetti, Sonia; Dorizzi, Bernadette; Khalil, M. I.; Moustafa, Mohamed N.; Abbas, Hazem M.; Muramatsu, Daigo; Yanıkoğlu, Berrin; Kholmatov, Alisher; Martinez-Diaz, Marcos; Fiérrez, Julián; Ortega‐Garcia, Javier; Roure-Alcobe, Josep; Fabregas, Joan; Faundez-Zanuy, Marcos; Pascual-Gaspar, J. M.; Cardeñoso-Payo, Valentı́n; Vivaracho-Pascual, Carlos

doi:10.1016/j.patcog.2011.08.008

Cited by 84 publications

(56 citation statements)

References 19 publications

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“…The final score is computed as the average of the scores obtained between the test signature and the enrolled samples. This system ranked among the top three algorithms in all the tasks of the recent BioSecure Signature Evaluation Campaign BSEC-2009 [24].…”

Section: On-line and Off-line Signature Recognition Systemsmentioning

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: On-line and Off-line Signature Recognition Systemsmentioning

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

View full text Add to dashboard Cite

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“…For the baseline signature verification system we chose the function-based local approach described in [15], which ranked among the top three algorithms at BSEC-2009 [9]. In this approach, 9 different time functions (selected using SFFS from a total of 34 features in [15]) are directly matched using an elastic technique, based on Dynamic Programming, known as Dynamic Time Warping (DTW) [15].…”

Section: On-line Signature Verification With Dtwmentioning

confidence: 99%

Enhanced on-line signature verification based on skilled forgery detection using Sigma-LogNormal Features

Gomez‐Barrero

Galbally

Fiérrez

et al. 2015

2015 International Conference on Biometrics (ICB)

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“…One of the objectives of BioSecure Signature Evaluation Campaign (BSEC'2009) was to study the influence of acquisition conditions (digitizing tablet or PDA) on authentication systems' performance [14]. Results are reported using signatures from 382 writers, acquired on a digitizing tablet and on a PDA, respectively.…”

Section: Signature Recognition On Mobile Devicesmentioning

confidence: 99%

“…One of the objectives of the BSEC'2009 competition was the evaluation of online signature algorithms with respect to the quality of the signatures [14].…”

Section: Signaturementioning

confidence: 99%

Online Signature Verification on MOBISIG Finger-Drawn Signature Corpus

Antal

Szabó

Tordai

2018

Mobile Information Systems

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We present MOBISIG, a pseudosignature dataset containing finger-drawn signatures from 83 users captured with a capacitive touchscreen-based mobile device. e database was captured in three sessions resulting in 45 genuine signatures and 20 skilled forgeries for each user. e database was evaluated by two state-of-the-art methods: a function-based system using local features and a feature-based system using global features. Two types of equal error rate computations are performed: one using a global threshold and the other using user-specific thresholds. e lowest equal error rate was 0.01% against random forgeries and 5.81% against skilled forgeries using user-specific thresholds that were computed a posteriori. However, these equal error rates were significantly raised to 1.68% (random forgeries case) and 14.31% (skilled forgeries case) using global thresholds. e same evaluation protocol was performed on the DooDB publicly available dataset. Besides verification performance evaluations conducted on the two finger-drawn datasets, we evaluated the quality of the samples and the users of the two datasets using basic quality measures. e results show that finger-drawn signatures can be used by biometric systems with reasonable accuracy.

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BioSecure signature evaluation campaign (BSEC'2009): Evaluating online signature algorithms depending on the quality of signatures

Cited by 84 publications

References 19 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

Enhanced on-line signature verification based on skilled forgery detection using Sigma-LogNormal Features

Online Signature Verification on MOBISIG Finger-Drawn Signature Corpus

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