Hybrid User-Independent and User-Dependent Offline Signature Verification with a Two-Channel CNN

Yılmaz, Mustafa Berkay; Ozturk, Kagan

doi:10.1109/cvprw.2018.00094

Cited by 40 publications

(18 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have used a CNN architecture that is inspired by Yilmarz et al [29] which was developed for an offline signature verification problem. We have modified the architecture to suit for online signature, which is of one dimension.…”

Section: Cnn and Siamese Architecturementioning

confidence: 99%

OSVNet: Convolutional Siamese Network for Writer Independent Online Signature Verification

Sekhar¹,

Mukherjee²,

Guru³

et al. 2019

Preprint

View full text Add to dashboard Cite

Online signature verification (OSV) is one of the most challenging tasks in writer identification and digital forensics. Owing to the large intra-individual variability, there is a critical requirement to accurately learn the intra-personal variations of the signature to achieve higher classification accuracy. To achieve this, in this paper, we propose an OSV framework based on deep convolutional Siamese network (DCSN). DCSN automatically extracts robust feature descriptions based on metric-based loss function which decreases intra-writer variability (Genuine-Genuine) and increases interindividual variability (Genuine-Forgery) and directs the DCSN for effective discriminative representation learning for online signatures and extend it for one shot learning framework. Comprehensive experimentation conducted on three widely accepted benchmark datasets MCYT-100 (DB1), MCYT-330 (DB2) and SVC-2004-Task2 demonstrate the capability of our framework to distinguish the genuine and forgery samples. Experimental results confirm the efficiency of deep convolutional Siamese network based OSV by achieving a lower error rate as compared to many recent and state-of-the art OSV techniques.

show abstract

Section: Cnn and Siamese Architecturementioning

confidence: 99%

OSVNet: Convolutional Siamese Network for Writer Independent Online Signature Verification

Sekhar¹,

Mukherjee²,

Guru³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Signature is the one of most powerful method for verification of the person. The signature verification system is widely used for identify the people via their handwritten signatures [2]. Many transactions for money around the world are occur daily.…”

Section: Introductionmentioning

confidence: 99%

An accurate signature verification system based on proposed HSC approach and ANN architecture

Kadhm

Mohammed

Ayad

2021

IJEECS

View full text Add to dashboard Cite

<span>With the rapid development of technology in all life fields, and due to the huge daily needs for banking systems process, documents processing and other similar systems. The authentication became more required key for these systems. One of the successful system to verify the any person is the signature verification system. However, a reliable and accurate system is still needed. For this reason, the security challenge is take place via authentic signatures. However, a reliable and accurate system is still needed. For this reason, the security challenge is take place via authentic signatures. Therefore, this paper present a reliable signature verification system using proposed histogram of sparse codes (HSC) feature extraction approach and artificial neural networks (ANN) architecture for classification. The system achieved fast computing 0.09 ms and accurate verification results that is 99.7% using three different signature images datasets CEDAR, UTSig, and ICDAR.</span>

show abstract

“…This kind of hybrid system [37] was used later in a WD classification stage. Another hybrid system was effective in [38] where the authors extracted features through WD scores of the SigNet-F representation [34] and final results were obtained by a fusion of WI and WD classifiers at score level.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Common Authorship of Signatures by Off-Line Automatic Signature Verification Without the Use of Reference Signatures

Diaz¹,

Ferrer

Ramalingam

et al. 2020

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

show abstract

Hybrid User-Independent and User-Dependent Offline Signature Verification with a Two-Channel CNN

Cited by 40 publications

References 10 publications

OSVNet: Convolutional Siamese Network for Writer Independent Online Signature Verification

OSVNet: Convolutional Siamese Network for Writer Independent Online Signature Verification

An accurate signature verification system based on proposed HSC approach and ANN architecture

Investigating the Common Authorship of Signatures by Off-Line Automatic Signature Verification Without the Use of Reference Signatures

Contact Info

Product

Resources

About