Offline signature verification and quality characterization using poset-oriented grid features

Zois, Elias N.; Alewijnse, Linda; Economou, George

doi:10.1016/j.patcog.2016.01.009

Cited by 73 publications

(36 citation statements)

References 47 publications

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“…3. The classifiers used like Neural Network, Hidden Markov Model, Support Vector Machine gives better performance in either acceptance or rejection rate in genuine or forgery signature but not in both cases [19,25].…”

Section: Motivation and Contribution Of The Proposed Workmentioning

confidence: 97%

“…An advance poset oriented grid based system is proposed by Elias [25]. In this work offline handwritten signature was modeled by targeting towards grid based lattices of simple and compound events of pixel assortments.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…Prakash et al [14] has used same database but different matching technique. Elias et al [25] achieved FAR of 24.23 % on different database by using SVM classifier. FAR of 14.18 % and FRR of 13.08 % has been reported by Nguyen et al [10] on GPDS-900 dataset.…”

Section: Rep Treementioning

confidence: 99%

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Verification of static signature pattern based on random subspace, REP tree and bagging

Chandra

Maheshkar

2017

Multimed Tools Appl

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In this paper we propose an effective method for static signature recognition from spontaneous handwritten text images. Our method relies on different aspects of writing: the presence of redundant patterns in the writing and its features. Signatures are analyzed at small fragments in which we seek to extract the patterns that an individual employs frequently as he writes. We exploit different features of writing like orientation, centroid and contour by computing a set of features from writing samples at different levels of observations. Orientation like intersecting point, edge point and gradient change of signature achieve great success in feature description. These features are extracted from the standard signature database and extracted features are trained and tested by machine learning (ML) approach. The machine learning approaches like Bagging, Random subspace (RS) and REP tree are used for classification purpose. Bagging with 10 iterations and base learner achieved efficiency upto 88 %. RS randomly selects features from feature set and creates new feature set. It uses decision tree as base classifier with different tree size. RS achieved efficiency same as Bagging but has more statistical errors. However, in case of REP tree we have achieved efficiency upto 75 %. The experimental results show that the Bagging and RS achieves promising results on publicly available data set.

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Section: Motivation and Contribution Of The Proposed Workmentioning

confidence: 97%

Section: Review Of Related Workmentioning

confidence: 99%

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Verification of static signature pattern based on random subspace, REP tree and bagging

Chandra

Maheshkar

2017

Multimed Tools Appl

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“…ey concluded that predicting the utility of a signature sample using a multifeature vector was possible. More recently, another novel method was proposed for the quality evaluation of off-line signatures [21].…”

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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“…In the analysis and verification of static signatures, Zois et al [14] presented a grid-based template matching scheme. In their study, the fine geometric structure of the signature is efficiently encoded with the grid template and partitioned into subsets.…”

Section: Introductionmentioning

confidence: 99%

An Improved Geo-Textural Based Feature Extraction Vector For Offline Signature Verification

Gyimah

Appati

Darkwah

et al. 2019

JAMCS

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In the field of pattern recognition, automatic handwritten signature verification is of the essence. The uniqueness of each person's signature makes it a preferred choice of human biometrics. However, the unavoidable side-effect is that they can be misused to feign data authenticity. In this paper, we present an improved feature extraction vector for offline signature verification system by combining features of grey level occurrence matrix (GLCM) and properties of image regions. In evaluating the performance of the proposed scheme, the resultant feature vector is tested on a support vector machine (SVM) with varying kernel functions. However, to keep the parameters of the kernel functions optimized, the sequential minimal optimization (SMO) and the least square method was used. Results of the study explained that the radial basis function (RBF) coupled with SMO best support the improved featured vector proposed.

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Offline signature verification and quality characterization using poset-oriented grid features

Cited by 73 publications

References 47 publications

Verification of static signature pattern based on random subspace, REP tree and bagging

Verification of static signature pattern based on random subspace, REP tree and bagging

Online Signature Verification on MOBISIG Finger-Drawn Signature Corpus

An Improved Geo-Textural Based Feature Extraction Vector For Offline Signature Verification

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