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

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...

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Bibliographic Details
Published in:IEEE access Vol. 12; pp. 22144 - 22157
Main Authors: Zheng, Jianbin, Chen, Ziyao, Huang, Liping, Gao, Yifan, Yu, Xiangxiang, Wang, Hui, Yang, Jiamei, Wang, Yu
Format: Journal Article
Language:English
Published: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Algorithms
Artificial neural networks
Classification algorithms
convolutional autoencoder-model verification
Convolutional neural networks
Deep learning
depth-wise separable convolution neural network
Feature extraction
Forgery
Handwritten signature verification
Machine learning
Neural networks
Online signature verification
Reduction
Rejection rate
Run time (computers)
Signatures
Training
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3355449