Signature verification based on fusion of on-line and off-line kernels

Mottl, Vadim; Lange, M. M.; Sulimova, Valentina; Yermakov, Andrii

doi:10.1109/icpr.2008.4761208

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…The classification based on Support Vector Machines (SVMs) has been used widely in many pattern recognition applications as the handwritten signature verification [8], [14]. The SVM is a learning method introduced by Vapnik et al [15], which tries to find an optimal hyperplane for separating two classes.…”

Section: ) Review Of Svmsmentioning

confidence: 99%

“…Nakanishi et al proposed a parameter combination in Dynamic Time Warping (DTW) domain [7] for on-line signature verification. Mottl et al proposed a combination algorithm of on-line and off-line kernels [8] for signature verification using SVM. Recently, combination of off-line image and dynamic information which are obtained from the same signature [9] has been proposed that exploit global and local information.…”

Section: Introductionmentioning

confidence: 99%

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Combination of off-line and on-line signature verification systems based on SVM and DST

Abbas

Chibani

2011

2011 11th International Conference on Intelligent Systems Design and Applications

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Abstract-The objective of this work is to present a signature verification system based on combination of off-line and online systems for managing conflict provided by the Support Vector Machine (SVM) classifiers. This system is basically divided into three parts: i) off-line verification stage, ii) on-line verification stage and iii) combination module using Dempster-Shafer theory (DST). The proposed framework allows combining the normalized SVM outputs and uses an estimation technique based on the dissonant model of Appriou to compute the belief assignments. Combination is performed using Dempster-Shafer (DS) rule followed by the likelihood ratio based decision making. Experiments are conducted on the well known NISDCC signature collection using false rejection and false acceptance criteria. The obtained results show that the proposed combination framework using DST yields the best verification accuracy compared to the sum rule even when individual off-line and on-line classifications provide conflicting results.

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Section: ) Review Of Svmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combination of off-line and on-line signature verification systems based on SVM and DST

Abbas

Chibani

2011

2011 11th International Conference on Intelligent Systems Design and Applications

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show abstract

“…Nakanishi et al proposed a parameter combination in Dynamic Time Warping (DWT) domain [4] for on-line signature verification. Mottl et al [5] proposed a combination algorithm of on-line and off-line kernels for signature verification using SVM. Recently, combination of off-line image and dynamic information which are obtained from the same signature [6] has been proposed that exploit global and local information.…”

Section: Introductionmentioning

confidence: 99%

SVM-DSmT combination for off-line signature verification

Abbas

Chibani

2012

2012 International Conference on Computer, Information and Telecommunication Systems (CITS)

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“…The feature selectivity of this SVM generalisation is parametrically determined by µ : 0 ≤ µ < ∞. As µ → 0 , variances tend toward unity (10), and the RKM degenerates to the classical SVM (2). Contrarily, when µ → ∞, we have from (6) that n i=1 (1/r i )a 2 i +(1+µ) ln r i + C N j=1 δ j → min; actually a significantly more selective training criterion than the original RKM (without supervised selectivity):…”

Section: The Continuous Training Technique With Supervised Selectivitymentioning

confidence: 99%

“…In the following paper, we show, following [9] and [10], how selectivity may be incorporated into the Relevance Kernel Machine (RKM) [4,5], a continuous wrapper FS method previously described by the authors. The desired selectivity is achieved through a meta-parameter that controls the tendency of the RKM to generate zero components in the orientation of the decision plane (and hence the degree of elimination of constituent kernels).…”

Section: Introductionmentioning

confidence: 99%

Supervised Selective Combining Pattern Recognition Modalities and Its Application to Signature Verification by Fusing On-Line and Off-Line Kernels

Tatarchuk

Sulimova

Windridge

et al. 2009

Multiple Classifier Systems

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Abstract. We consider the problem of multi-modal pattern recognition under the assumption that a kernel-based approach is applicable within each particular modality. The Cartesian product of the linear spaces into which the respective kernels embed the output scales of single sensors is employed as an appropriate joint scale corresponding to the idea of combining modalities at the sensor level. This contrasts with the commonly adopted method of combining classifiers inferred from each specific modality. However, a significant risk in combining linear spaces is that of overfitting. To address this, we set out a stochastic method for encompassing modal-selectivity that is intrinsic to (that is to say, theoretically contiguous with) the selected kernel-based pattern-recognition approach. The principle of kernel selectivity supervision is then applied to the problem of signature verification by fusing several on-line and off-line kernels into a complete training and verification technique.

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Signature verification based on fusion of on-line and off-line kernels

Cited by 8 publications

References 14 publications

Combination of off-line and on-line signature verification systems based on SVM and DST

Combination of off-line and on-line signature verification systems based on SVM and DST

SVM-DSmT combination for off-line signature verification

Supervised Selective Combining Pattern Recognition Modalities and Its Application to Signature Verification by Fusing On-Line and Off-Line Kernels

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