SVM-DSmT combination for off-line signature verification

Abbas, Nassim; Chibani, Youcef

doi:10.1109/cits.2012.6220365

Cited by 12 publications

(13 citation statements)

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“…They proposed a robust face recognition system using an integrated framework of fusion at image level and score level of visible and infrared face images. Vatsa et al [37,38] and Abbas et al [39] have also used DSmT-based fusion in various multibiometric systems. Vatsa et al [37] incorporated quality factor in the DSmT framework for fingerprint verification using level-2 and level-3 features.…”

Section: Related Workmentioning

confidence: 99%

“…In another work, Vatsa et al [38] proposed a dynamic reconciliation scheme for biometric fusion to handle various scenarios occurring in the probe dataset. Abbas et al [39] proposed DSmT-based framework of fusion for handwritten signature verification. In [40,41], subjective logic fusion operator is introduced in biometric fusion via belief fusion.…”

Section: Related Workmentioning

confidence: 99%

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Score‐level fusion using generalized extreme value distribution and DSmT, for multi‐biometric systems

2018

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Human recognition in a multi-biometric system is performed by combining biometric clues from different sources (multiple sensors, units, algorithms, samples and modalities) at different levels (sensor, feature, score, rank and decision level). Low computational complexity and adequate data for fusion make the score-level fusion a preferable option over other levels of fusion. However, incompatibility issue prevails at this level as scores obtained from different uni-biometric systems are disparate in nature. This disparity can be resolved by using score normalisation before fusion. This study first analysed the effect of generalised extreme value distribution-based score normalisation technique on different fusion techniques and then proposes an efficient score fusion technique based on Dezert-Smarandache theory (DSmT). A unique blend of belief assignment and decision-making methods in the DSmT framework is proposed for score-level fusion. For evaluation of the proposed method, experiments are performed on multi-algorithm, multi-unit and multi-modal biometrics systems created from three publicly available datasets: (i) NIST BSSR1 multi-biometric score database, (ii) face recognition grand challenge (FRGC) V2.0 and (iii) LG4000 iris dataset. Comparative studies of performance analysis show the efficiency of the authors' proposed method over other recently published state-of-the-art fusion methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Score‐level fusion using generalized extreme value distribution and DSmT, for multi‐biometric systems

2018

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“…Although combining multiple classifiers is a widely used technique in the field of signature verification, to the best of the authors' knowledge, the degree of uncertainty that this technique may introduce to the classification problem has not been significantly explored. In fact, there exist few works in the literature taking into account such situation resorting to the BFT [27,28]. In [27], the authors propose the fusion of online and offline information using SVM classifiers, and in [28], the fusion of SVM classifiers is proposed to combine different feature sets for offline verification.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, there exist few works in the literature taking into account such situation resorting to the BFT [27,28]. In [27], the authors propose the fusion of online and offline information using SVM classifiers, and in [28], the fusion of SVM classifiers is proposed to combine different feature sets for offline verification. In this paper, the combination of three different and meaningful feature sets, selected by different criteria, is proposed on the basis of a score level fusion approach based on the BFT, which would provide the appropriate framework to quantify the confidence in each of the classifiers, and to handle the conflicting results that may appear.…”

Section: Introductionmentioning

confidence: 99%

Online Signature Verification: To What Extent Should a Classifier be Trusted in?

Parodi¹,

Gómez²

2017

CLEIej

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To select the best features to model the signatures is one of the major challenges in the field of online signature verification. To combine different feature sets, selected by different criteria, is a useful technique to address this problem. In this line, the analysis of different features and their discriminative power has been researchers' main concern, paying less attention to the way in which the different kind of features are combined. Moreover, the fact that conflicting results may appear when several classifiers are being used, has rarely been taken into account. In this paper, a score level fusion scheme is proposed to combine three different and meaningful feature sets, viz., an automatically selected feature set, a feature set relevant to Forensic Handwriting Experts (FHEs), and a global feature set. The score level fusion is performed within the framework of the Belief Function Theory (BFT), in order to address the problem of the conflicting results appearing when multiple classifiers are being used. Two different models, namely, the Denoeux and the Appriou models, are used to embed the problem within this framework, where the fusion is performed resorting to two well-known combination rules, namely, the Dempster-Shafer (DS) and the Proportional Conflict Redistribution (PCR5) one. In order to analyze the robustness of the proposed score level fusion approach, the combination is performed for the same verification system using two different classification techniques, namely, Ramdon Forests (RF) and Support Vector Machines (SVM). Experimental results, on a publicly available database, show that the proposed score level fusion approach allows the system to have a very good trade-off between verification results and reliability.

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“…Abbas et al [11] have proposed a system for managing SVM classifier conflicts. This system based on the decision combination, it consists of three different modules i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

An Appraisal of Off-line Signature Verification Techniques

Shah

Khan²,

Shah

2015

IJMECS

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Abstract-Biometrics is being commonly used nowadays for the identification and verification of humans everywhere in the world. In biometrics humans unique characteristics like palm, fingerprints, iris etc. are being used. Pattern Recognition and image processing are the major areas where research on signature verification is carried out. Hand written Signature of an individual is also unique and for identification of humans are being used and accepted specially in the banking and other financial transactions. The hand written signatures due to its importance are at target of fraudulence. In this paper we have surveyed different papers on techniques that are currently used for the identification and verification of Offline signatures.

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SVM-DSmT combination for off-line signature verification

Cited by 12 publications

References 13 publications

Score‐level fusion using generalized extreme value distribution and DSmT, for multi‐biometric systems

Score‐level fusion using generalized extreme value distribution and DSmT, for multi‐biometric systems

Online Signature Verification: To What Extent Should a Classifier be Trusted in?

An Appraisal of Off-line Signature Verification Techniques

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