Multi-scale residual based siamese neural network for writer-independent online signature verification

Qi, Shanshan; Luan, Fangjun; Yuan, Shuai

doi:10.1007/s10489-022-03318-5

Cited by 9 publications

(3 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The feature extraction process of the convolutional neural network heavily relies on the parameters of its convolutional layers, which are applied layer by layer [19]. The main purpose of this article is to conduct experimental comparisons and…”

Section: ) Dwscnn Parameter Discussionmentioning

confidence: 99%

DWSCNN Online Signature Verification Algorithm Based on CAE-MV Feature Dimensionality Reduction

Zheng,

Chen,

Huang

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In recent years, with the rapid advancements in deep learning technologies, particularly deep neural networks, signature verification has seen significant improvements in accuracy. Despite the significant progress made in using deep learning technologies, there remains several challenges that affect their practical application, such as insufficient feature extraction, long neural network computation time, and high resource consumption. In this paper, an online handwritten signature verification algorithm is proposed, which mainly uses the CAE-MV-based feature dimensionality reduction method to compress and select features of the original signature data to construct a signature feature set. The Depth-wise Separable Convolutional Neural Network(DWSCNN) based on Depth-wise separable convolution is used to classify and verify the signature feature set. Compared with CNN, the DWSCNN can significantly reduce the number of neural network parameters while the classification effect is not much different, thus greatly shortening the running time and resource occupation. Through the analysis of the verification results on two publicly available signature databases, MCYT-100 and SVC-2004, the proposed algorithm improves the accuracy, reduces the False Acceptance Rate (FAR) and False Rejection Rate (FRR). The average verification accuracy reaches 98.92%, which is superior to the current mainstream online signature verification frameworks, demonstrating the effectiveness and efficiency of the proposed framework.

show abstract

Section: ) Dwscnn Parameter Discussionmentioning

confidence: 99%

DWSCNN Online Signature Verification Algorithm Based on CAE-MV Feature Dimensionality Reduction

Zheng,

Chen,

Huang

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…After obtaining the correlation coefficients between the new feature vectors, we introduced the Gaussian density function to model these correlation coefficients, as shown in Equation (6).…”

Section: Online Signature Verificationmentioning

confidence: 99%

“…This approach facilitates dimensionality reduction in feature data, leading to enhanced accuracy in signature verification. Shen et al [6] developed a Siamese network framework integrated with a multi-scale attention mechanism to extract efficient features from a limited number of signature samples. The findings demonstrated that the equal error rate (EER) for signature verification in the MCYT-100 dataset was 6.57%.…”

Section: Introductionmentioning

confidence: 99%

Online Handwritten Signature Verification Method Based on Uni-Feature Correlation Coefficient between Signatures

Liu,

Xin

2023

Sensors

View full text Add to dashboard Cite

Online handwritten signature verification is a crucial direction of research in the field of biometric recognition. Recently, many studies concerning online signature verification have attempted to improve performance using multi-feature fusion. However, few studies have provided the rationale for selecting a certain uni-feature to be fused, and few studies have investigated the contributions of a certain uni-feature in the multi-feature fusion process. This lack of research makes it challenging for future researchers in related fields to gain inspiration. Therefore, we use the uni-feature as the research object. In this paper, the uni-feature is one of the X and Y coordinates of the signature trajectory point, pen pressure, pen tilt, and pen azimuth feature. Aiming to solve the unequal length of feature vectors and the low accuracy of signature verification when using uni-features, we innovatively introduced the idea of correlation analysis and proposed a dynamic signature verification method based on the correlation coefficient of uni-features. Firstly, an alignment method of two feature vector lengths was proposed. Secondly, the correlation coefficient calculation formula was determined by analyzing the distribution type of the feature data, and then the correlation coefficient of the same uni-feature between the genuine signatures or between the genuine and forged signatures was calculated. Finally, the signature was verified by introducing a Gaussian density function model and combining it with the signature verification discrimination threshold. Experimental results showed that the proposed method could improve the performance of dynamic signature verification based on uni-features. In addition, the pen pressure feature had the best signature verification performance, with the highest signature verification accuracy of 93.46% on the SVC 2004 dataset.

show abstract

A Multifaceted Role of Biometrics in Online Security, Privacy, and Trustworthy Decision Making

Gavrilova

Anzum²,

Bari³

et al. 2022

Breakthroughs in Digital Biometrics and Forensics

View full text Add to dashboard Cite

Multi-scale residual based siamese neural network for writer-independent online signature verification

Cited by 9 publications

References 57 publications

DWSCNN Online Signature Verification Algorithm Based on CAE-MV Feature Dimensionality Reduction

DWSCNN Online Signature Verification Algorithm Based on CAE-MV Feature Dimensionality Reduction

Online Handwritten Signature Verification Method Based on Uni-Feature Correlation Coefficient between Signatures

A Multifaceted Role of Biometrics in Online Security, Privacy, and Trustworthy Decision Making

Contact Info

Product

Resources

About