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International Journal of Soft Computing And Artificial Intelligence (IJSCAI)-IJSCAI
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Volume Issue
Volume-7,Issue-2  ( Nov, 2019 )
Paper Title
Signature Verification Using Convolutional Neural Network
Author Name
Anagha Bhat, Bharathi Gummanur, Likhitha Priya
Student, Dayananda Sagar College Of Engineering, Shavige Malleshwara Hills, 91st Main Rd, 1st Stage, Kumaraswamy Layout, Bengaluru, Karnataka 560078
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
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