Deep Fuse OSV: Online signature verification using hybrid feature fusion and depthwise separable convolution neural network architecture
IET Biometrics, ISSN: 2047-4946, Vol: 9, Issue: 6, Page: 259-268
2020
- 20Citations
- 19Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Online signature verification (OSV) is a widely utilised technique in the medical, e-commerce and m-commerce applications to lawfully bind the user. These high-speed systems demand faster writer verification with a limited amount of information along with restrictions on training and storage cost. This study makes two major contributions: (i) A competent feature fusion technique in which traditional statistical-based features are fused with deep representations from a convolutional auto-encoder; and (ii) a hybrid architecture combining depth-wise separable convolution neural network (DWSCNN) and long short term memory (LSTM) network delivering state-of-the-art performance for OSV is proposed. DWSCNN is utilised for extracting deep feature representations and LSTM is competent in learning long term dependencies of stroke points of a signature. This hybrid combination accomplishes better classification accuracy (lower error rates) even with one-shot learning, i.e. achieving higher classification accuracies with only one training signature sample per user. The authors have extensively evaluated their model using three widely used datasets MCYT-100, SVC and SUSIG. These exhaustive experimental studies confirm that the DeepFuseOSV framework results in the state-of-the-art outcome by achieving an equal error rate (EER) of 13.26, 2.58, 0.07% in Skilled 1, Skilled 10 and Random 10 categories of MCYT-100, respectively, 7.71% in Skilled 1 category of SVC, 1.70% in Random 1 category of SUSIG.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85096500908&origin=inward; http://dx.doi.org/10.1049/iet-bmt.2020.0032; https://onlinelibrary.wiley.com/doi/10.1049/iet-bmt.2020.0032; https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-bmt.2020.0032; https://onlinelibrary.wiley.com/doi/full-xml/10.1049/iet-bmt.2020.0032; https://dx.doi.org/10.1049/iet-bmt.2020.0032; https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-bmt.2020.0032
Institution of Engineering and Technology (IET)
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