OSVFuseNet: Online Signature Verification by feature fusion and depth-wise separable convolution based deep learning
Autor: | Rama Krishna Sai Subrahmanyam Gorthi, Chandra Sekhar Vorugunti, Prerana Mukherjee, Viswanath Pulabaigari |
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Rok vydání: | 2020 |
Předmět: |
0209 industrial biotechnology
Computer science business.industry Cognitive Neuroscience Deep learning Pattern recognition 02 engineering and technology Convolutional neural network Autoencoder Signature (logic) Computer Science Applications Convolution Set (abstract data type) 020901 industrial engineering & automation Artificial Intelligence Feature (computer vision) 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Artificial intelligence business |
Zdroj: | Neurocomputing. 409:157-172 |
ISSN: | 0925-2312 |
Popis: | 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. |
Databáze: | OpenAIRE |
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