Offline Signature Verification Using Critical Region Matching

Bansal, Abhay; Nemmikanti, P.; Kumar, Pradeep

doi:10.1109/fgcns.2008.55

Cited by 22 publications

(17 citation statements)

References 9 publications

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“…A number of structural (i.e. string, tree, or graph-based) signature verification frameworks have been proposed in the last decade [13][14][15][16][17]. Graphs, actually the most general data structure, allow to represent the inherent topological handwriting characteristics of signatures in a natural and comprehensive way.…”

Section: Introductionmentioning

confidence: 99%

Graph Embedding for Offline Handwritten Signature Verification

Stauffer

Maergner

Fischer

et al. 2019

Proceedings of the 2019 3rd International Conference on Biometric Engineering and Applications

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Due to the high availability and applicability, handwritten signatures are an eminent biometric authentication measure in our life. To mitigate the risk of a potential misuse, automatic signature verification tries to distinguish between genuine and forged signatures. Most of the available signature verification approaches make use of vectorial rather than graph-based representations of the handwriting. This is rather surprising as graphs offer some inherent advantages. Graphs are, for instance, able to directly adapt their size and structure to the size and complexity of the respective handwritten entities. Moreover, several fast graph matching algorithms have been proposed recently that allow to employ graphs also in domains with large amounts of data. The present paper proposes to use different graph embedding approaches in conjunction with a recent graph-based signature verification framework. That is, signature graphs are not directly matched with each other, but first compared with a set of predefined prototype graphs, in order to obtain a dissimilarity representation. In an experimental evaluation, we employ the proposed method on two widely used benchmark datasets. On both datasets, we empirically confirm that the learning-free graph embedding outperforms state-of-the-art methods with respect to both accuracy and runtime.

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Section: Introductionmentioning

confidence: 99%

Graph Embedding for Offline Handwritten Signature Verification

Stauffer

Maergner

Fischer

et al. 2019

Proceedings of the 2019 3rd International Conference on Biometric Engineering and Applications

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“…The graph based approach has two limitations: (1) It works on relatively small window sizes (32*64) and (2) It doesn't work when the test signature is a superset of the original signature. Based on geometrical shape of the critical regions of the signature, a graph matching based signature verification technique is introduced in [13]. The comparison of two signatures is reduced to the comparison of their respective graph representations.…”

Section: Previous Workmentioning

confidence: 99%

Offline Signature Verification for Authentication

Jana¹,

Mandal²,

Chhaya³

2015

IJCA

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Biometrics verification has become a recent trend to prevent unauthorized accesses to all kinds of e-data. Signature is strongly accepted in legally and socially as identification and authentication of a person's identity. But, it is very difficult to verify the signature physically. So, it is needed to design a system that verifies the signature of a human automatically. A set of actual signatures is collected from individuals whose signatures have to be authenticated by the system. The topological and texture features are extracted from the actual signature set. The system is trained by using these features. The mean feature values of all the actual signature features are calculated. This mean features acts as the model for verification against a test signature. Euclidian distance between template signature features and claimed signature features serves as a measure of similarity between the two. If this distance is greater than a predefined threshold, then the test signature is detected as fake. The system provides the result to classify actual and forgery signature with accuracy up to 100%.

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“…In a sliding window, the center point is , the max value is and the min value is , where represents all of the pixel values. The definition of Bernsen algorithm in sliding window is (1) Then, the binarization result can be obtained by (2) The shortcoming of Bersen algorithm is over-segmentation in uniform brightness regions. So it is not reasonable to get a threshold image by equation (2) when the difference between the max and min is too small.…”

Section: Contour Detection Preprocessingmentioning

confidence: 99%

“…Text-dependent methods require writers to write the same fixed text with the training handwritings. Signature verification [2,3], which is widely used in credit cards, is one of text-dependent writer identification. In text-independent methods, any handwriting documents with different text will be useful.…”

Section: Introductionmentioning

confidence: 99%