Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction
- Authors
- Nam, Seungsoo; Park, Hosung; Seo, Changho; Choi, Daeseon
- Issue Date
- Feb-2018
- Publisher
- MDPI
- Keywords
- dynamic signature; convolution neural network; autoencoder neural network; skilled forgery; biometric
- Citation
- APPLIED SCIENCES-BASEL, v.8, no.2
- Journal Title
- APPLIED SCIENCES-BASEL
- Volume
- 8
- Number
- 2
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/39722
- DOI
- 10.3390/app8020153
- ISSN
- 2076-3417
- Abstract
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Information Technology > School of Software > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.