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Appl. Sci. 2018, 8(2), 153; doi:10.3390/app8020153

Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction

Department of Convergence Science, Kongju National University, Chungnam 32588, Korea
Department of Medical Information, Kongju National University, Chungnam 32588, Korea
Author to whom correspondence should be addressed.
Received: 8 December 2017 / Revised: 17 January 2018 / Accepted: 19 January 2018 / Published: 23 January 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
View Full-Text   |   Download PDF [1298 KB, uploaded 23 January 2018]   |  


This paper proposes a dynamic verification scheme for finger-drawn signatures in smartphones. As a dynamic feature, the movement of a smartphone is recorded with accelerometer sensors in the smartphone, in addition to the moving coordinates of the signature. To extract high-level longitudinal and topological features, the proposed scheme uses a convolution neural network (CNN) for feature extraction, and not as a conventional classifier. We assume that a CNN trained with forged signatures can extract effective features (called S-vector), which are common in forging activities such as hesitation and delay before drawing the complicated part. The proposed scheme also exploits an autoencoder (AE) as a classifier, and the S-vector is used as the input vector to the AE. An AE has high accuracy for the one-class distinction problem such as signature verification, and is also greatly dependent on the accuracy of input data. S-vector is valuable as the input of AE, and, consequently, could lead to improved verification accuracy especially for distinguishing forged signatures. Compared to the previous work, i.e., the MLP-based finger-drawn signature verification scheme, the proposed scheme decreases the equal error rate by 13.7%, specifically, from 18.1% to 4.4%, for discriminating forged signatures. View Full-Text
Keywords: dynamic signature; convolution neural network; autoencoder neural network; skilled forgery; biometric dynamic signature; convolution neural network; autoencoder neural network; skilled forgery; biometric

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Nam, S.; Park, H.; Seo, C.; Choi, D. Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction. Appl. Sci. 2018, 8, 153.

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