Paper Info
Title
OSVFuseNet: Online Signature Verification by feature fusion and depth-wise separable convolution based deep learning
Abstract
Online Signature Verification (OSV) techniques have been deployed in production systems for decades, yet training the model for efficient classification of the test signature from fewer training signature samples is still an open challenge. The advancements in Convolutional Neural Networks (CNNs) enormously boosted the effectiveness of OSV systems. However, learning subtle and discriminating representations from few training samples to classify the genuineness of test signature has not been explored fully. In this paper, a Convolution Autoencoder (CAE) is used to obtain high-level feature representations from the input signature and these high level features are fused with handcrafted features to constitute a hybrid feature set. The hybrid set of features is presented as an input to an Online Signature Verification framework made up of Depth-wise Separable Convolutional Neural Network (DWSCNN). DWSCNN effectively learn deep feature representations with fewer training samples and parameters than traditional CNNs resulting in a light weight OSV framework. Thorough experimental analysis on three benchmark datasets MCYT-100 (DB1), SVC-2004-Task2 and SUSIG-Visual corpus confirm that the proposed hybrid fusion of feature set and DWSCNN based OSV framework achieve higher classification accuracies and outperforms many contemporary and state-of-the art OSV models.
Year
DOI
Venue
2020
10.1016/j.neucom.2020.05.072
Neurocomputing
Keywords
DocType
Volume
Online Signature Verification,Depth-wise separable convolutional neural networks,Few shot learning,Autoencoders
Journal
409
ISSN
Citations 
PageRank 
0925-2312
2
0.36
References 
Authors
0
4
Name
Order
Citations
PageRank
Chandra Sekhar Vorugunti120.36
P. Viswanath214811.77
Rama Krishna Sai Subrahmanyam Gorthi322.05
prerana mukherjee41010.94