International Journal of Soft Computing And Artificial Intelligence (IJSCAI)
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current issues
Volume-7,Issue-2  ( Nov, 2019 )
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Sep. 2020
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 14
Paper Published : 188
No. of Authors : 530
  Journal Paper

Paper Title
Signature Verification Using Convolutional Neural Network

Abstract
A number of existing authentication systems use biometric information, more specifically, signatures, for authentication. However, authentication based on signatures is not infallible, as it is possible to forge signatures with a convincing degree of similarity. There needs to be a reliable method of detecting fake signatures in order to avoid forgery and fraud. In the proposed method, we use a convolutional neural network as a binary classifier. The datasets used were sourced from SigComp 2011 and Kaggle. The network is made to learn and extract features from the signatures that are pre-labelled either as fake or genuine. This network is then tested on a previously unseen set of signatures. This method gave varying results; the accuracy achieved from the method described varied in the range 15-60%. There could be a significant improvement in the results in case of availability of larger datasets for training. Keywords - Signature Verification, Convolutional Neural Networks


Author - Anagha Bhat, Bharathi Gummanur, Likhitha Priya

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| Published on 2019-12-04
   
   
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