Hierarchical Random Graph Model for Off-line Handwritten Signatures Recognition

Piekarczyk, Marcin

doi:10.1109/cisis.2010.132

Cited by 14 publications

(27 citation statements)

References 7 publications

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“…On the other side, using the template matching model faced with the issue of testing the graph isomorphism in the effective way. In spite of that, some proposals of HSV systems basing on graph-based modeling has been appeared in the recent years including [16] for on-line and [12] for off-line signatures recognition. In both cases the graph-based modeling is used to handle the distinctive features connected with handwritten signatures.…”

Section: Introductionmentioning

confidence: 97%

“…In [12] it is done by utilization of the random graphs while in [16] the classifier is responsible to handle it.…”

Section: Introductionmentioning

confidence: 99%

“…The paper [12] introduces a mathematical linguistic based model designed for distorted or ambiguous patterns, where a graph based approach is used for structure representation. The concept basing on the use of hierarchical random IE graphs and two-level probabilistic grammars belonging to the ETPL(k) class with the aim of gathering and analyzing the knowledge about the structure and the features of ambiguous patterns (signatures).…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the usage of attributed graphs enables the storage of additional semantic information describing the local properties of the signature. The graph linguistic formalisms applied -the IE graphs and attribute-controlled ETPL(k) grammar [9,12] -are characterized by considerable descriptive strength and a polynomial membership problem of the syntactic analysis. However, the effective mechanism of machine learning through grammatical inference process has been defined [9,13].…”

Section: Introductionmentioning

confidence: 99%

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Matrix-Based Hierarchical Graph Matching in Off-Line Handwritten Signatures Recognition

Piekarczyk

Ogiela

2013

2013 2nd IAPR Asian Conference on Pattern Recognition

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In this paper, a graph-based off-line handwritten signature verification system is proposed. The system can automatically identify some global and local features which exist within different signatures of the same person. Based on these features it is possible to verify whether a signature is a forgery or not. The structural description in the form of hierarchical attributed random graph set is transformed into matrix-vector structures. These structures can be directly used as matching pattern when examined signature is analyzed. The proposed approach can be applied to off-line signature verification systems especially for kanji-like or ideogram-based structurally complex signatures.

show abstract

Section: Introductionmentioning

confidence: 97%

“…In [12] it is done by utilization of the random graphs while in [16] the classifier is responsible to handle it.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Matrix-Based Hierarchical Graph Matching in Off-Line Handwritten Signatures Recognition

Piekarczyk

Ogiela

2013

2013 2nd IAPR Asian Conference on Pattern Recognition

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“…All these inter/intrapersonal morphological variations linked to the handwritten signature, added to the falsifications, make modelling this very particular image an intricate task. Many systems have been developed in order to model and verify the image of the signature [5,7,8,11,12,14,19,20,23,24,25]. In our case, we have opted for planar modelling based on neural networks.…”

Section: The False Signaturesmentioning

confidence: 99%

A Neuronal Planar Modeling for Handwriting Signature based on Automatic Segmentation

AbrougBenAbdelghani¹,

Amara²

2012

IJCA

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This paper deals with offline handwriting signature verification. We propose a planar neuronal model of signature image. Planar models are generally based on delimiting homogenous zones of images; we propose in this paper an automatic segmentation approach into bands of signature images. Signature image is modeled by a planar neuronal model with horizontal secondary models and a vertical principal model. The proposed method has been tested on two databases. The first is the one we have collected; it includes 6000 signatures corresponding to 60 writers. The second is the public GPDS-300 database including 16200 signature corresponding to 300 persons. The achieved results are promising.

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Features-Level Fusion of Face and Handwritten Signature in Multimodal Biometric Identification System

Khuwaja

2013

Cybernetics and Systems

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Hierarchical Random Graph Model for Off-line Handwritten Signatures Recognition

Cited by 14 publications

References 7 publications

Matrix-Based Hierarchical Graph Matching in Off-Line Handwritten Signatures Recognition

Matrix-Based Hierarchical Graph Matching in Off-Line Handwritten Signatures Recognition

A Neuronal Planar Modeling for Handwriting Signature based on Automatic Segmentation

Features-Level Fusion of Face and Handwritten Signature in Multimodal Biometric Identification System

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