Graph Embedding for Offline Handwritten Signature Verification

Stauffer, Michael; Maergner, Paul; Fischer, Andreas; Riesen, Kaspar

doi:10.1145/3345336.3345346

Cited by 4 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this advantage comes at the expense of increased complexity in basic dissimilarity assessments [27]. The authors in [28] focused on dissimilarity-based graph embedding techniques for signature verification. It generated n-dimensional feature representations for graphs, which were then used to classify signatures.…”

Section: Related Workmentioning

confidence: 99%

A Genetic Algorithm Based One Class Support Vector Machine Model for Arabic Skilled Forgery Signature Verification

et al. 2023

View full text Add to dashboard Cite

Recently, signature verification systems have been widely adopted for verifying individuals based on their handwritten signatures, especially in forensic and commercial transactions. Generally, feature extraction and classification tremendously impact the accuracy of system authentication. Feature extraction is challenging for signature verification systems due to the diverse forms of signatures and sample circumstances. Current signature verification techniques demonstrate promising results in identifying genuine and forged signatures. However, the overall performance of skilled forgery detection remains rigid to deliver high contentment. Furthermore, most of the current signature verification techniques demand a large number of learning samples to increase verification accuracy. This is the primary disadvantage of using deep learning, as the figure of signature samples is mainly restricted to the functional application of the signature verification system. In addition, the system inputs are scanned signatures that comprise noisy pixels, a complicated background, blurriness, and contrast decay. The main challenge has been attaining a balance between noise and data loss, since some essential information is lost during preprocessing, probably influencing the subsequent stages of the system. This paper tackles the aforementioned issues by presenting four main steps: preprocessing, multifeature fusion, discriminant feature selection using a genetic algorithm based on one class support vector machine (OCSVM-GA), and a one-class learning strategy to address imbalanced signature data in the practical application of a signature verification system. The suggested method employs three databases of signatures: SID-Arabic handwritten signatures, CEDAR, and UTSIG. Experimental results depict that the proposed approach outperforms current systems in terms of false acceptance rate (FAR), false rejection rate (FRR), and equal error rate (EER).

show abstract

Section: Related Workmentioning

confidence: 99%

A Genetic Algorithm Based One Class Support Vector Machine Model for Arabic Skilled Forgery Signature Verification

et al. 2023

View full text Add to dashboard Cite

show abstract

“…For thirty two users, accuracy of 100% is achieved. Graph embedding method with two dataset is used in the work [7]. fed to Raspberry pi.…”

Section: Reviewmentioning

confidence: 99%

Offline Signature Recognition u sing Pretrained Convolution Neural Network Model

Kumari¹,

Rana²

2019

IJEAT

View full text Add to dashboard Cite

Offline Signature recognition plays an important role in Forensic issues. In this paper, we explore Signature Identification and Verification using features extracted from pretrained Convolution Neural Network model (Alex Net). All the experiments are performed on signatures from three dataset (SigComp2011) (Dutch, Chinese), SigWiComp2013 (Japanese) and SigWIcomp2015 (Italian). The result shows that features extracted from pretrained Deep Convolution neural network and SVM as classifier show better results than that of Decision Tree. The accuracy of more than 96% for Japanese, Italian, Dutch and Chinese Signatures is obtained with Deep Convolution neural network and SVM as classifier.

show abstract

A Survey of State of the Art Methods Employed in the Offline Signature Verification Process

Stauffer

Maergner

Fischer

et al. 2020

New Trends in Business Information Systems and Technology

View full text Add to dashboard Cite

Graph Embedding for Offline Handwritten Signature Verification

Cited by 4 publications

References 29 publications

A Genetic Algorithm Based One Class Support Vector Machine Model for Arabic Skilled Forgery Signature Verification

A Genetic Algorithm Based One Class Support Vector Machine Model for Arabic Skilled Forgery Signature Verification

Offline Signature Recognition u sing Pretrained Convolution Neural Network Model

A Survey of State of the Art Methods Employed in the Offline Signature Verification Process

Contact Info

Product

Resources

About