Open AccessJournal Article
Handwritten signature identification using basic concepts of graph theory
TLDR
Previous work in the field of signature and writer identification is presented to show the historical development of the idea and a new promising approach in handwritten signature identification based on some basic concepts of graph theory is defined.Abstract:
Handwritten signature is being used in various applications on daily basis. The problem arises when someone decides to imitate our signature and steal our identity. Therefore, there is a need for adequate protection of signatures and a need for systems that can, with a great degree of certainty, identify who is the signatory. This paper presents previous work in the field of signature and writer identification to show the historical development of the idea and defines a new promising approach in handwritten signature identification based on some basic concepts of graph theory. This principle can be implemented on both on-line handwritten signature recognition systems and off-line handwritten signature recognition systems. Using graph norm for fast classification (filtration of potential users), followed by comparison of each signature graph concepts value against values stored in database, the system reports 94.25% identification accuracy.read more
Citations
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A Review of Finger-Vein Biometrics Identification Approaches
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TL;DR: The approaches taken from other researches on preprocessing, feature extraction and classification stage specifically for recognizing individual identity for biometrics trait using finger-vein are discussed.
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Combining graph edit distance and triplet networks for offline signature verification
Paul Maergner,Vinaychandran Pondenkandath,Michele Alberti,Marcus Liwicki,Kaspar Riesen,Rolf Ingold,Andreas Fischer +6 more
TL;DR: Offline signature verification is a challenging pattern recognition task where a writer model is inferred using only a small number of genuine signatures using a combination of complementary writer mode and reader mode.
Book ChapterDOI
A Novel Graph Database for Handwritten Word Images
TL;DR: Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Patternrecognition (SSPR), and S+S SPR 2016: Structural, Syntactic, and Statistical pattern recognition.
Book ChapterDOI
Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks
Paul Maergner,Vinaychandran Pondenkandath,Michele Alberti,Marcus Liwicki,Kaspar Riesen,Rolf Ingold,Andreas Fischer +6 more
TL;DR: This work proposes to complement a recent structural approach to offline signature verification based on graph edit distance with a statistical approach based on metric learning with deep neural networks, and demonstrates that combining the structural and statistical models leads to significant improvements in performance.
Proceedings ArticleDOI
A Structural Approach to Offline Signature Verification Using Graph Edit Distance
TL;DR: A novel structural approach to offline signature verification using an efficient cubic-time approximation of graph edit distance is introduced and several ways of creating, normalizing, and comparing signature graphs built from keypoints are put forward.
References
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Proceedings Article
Off-line Handwritten Signature Identification using Grid Gabor Feature and Support Vector Machine
TL;DR: This method uses Gabor Wavelet Transform (GWT) as feature extractor and Support Vector Machine (SVM) as classifier and main characteristic of proposed method is independency to nation of signers.