JOURNAL ARTICLE

Signify: Signature Verification Technique using Convolutional Neural Network

Alexandra Mae C. LayloMark Daryl A. DecilloLouie Andrew F. BooJeffrey S. Sarmiento

Year: 2019 Journal:   International Journal of Recent Technology and Engineering (IJRTE) Vol: 8 (2)Pages: 1763-1767

Abstract

Signature is one of the biometric traits that are being used in person authentication and due to its dominant usage; it became one of the top subjects of forgery. In this study, a signature verification using Convolutional Neural Network (CNN) is proposed. With the use of transfer learning, inception-v3 is mainly used for the feature extraction of data samples and for classification of signatures. The proposed method is assessed on dataset of handwritten signatures gathered from 4 people with 100 signatures each. The testing results determine the threshold value which is 96.43%. Factors that affect the accuracy of the result were also identified.

Keywords:
Signature (topology) Convolutional neural network Computer science Biometrics Pattern recognition (psychology) Artificial intelligence Authentication (law) Feature extraction Feature (linguistics) Artificial neural network Mathematics Computer security

Metrics

3
Cited By
0.21
FWCI (Field Weighted Citation Impact)
0
Refs
0.58
Citation Normalized Percentile
Is in top 1%
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Citation History

Topics

Handwritten Text Recognition Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Computer Science and Engineering
Physical Sciences →  Computer Science →  Artificial Intelligence
Vehicle License Plate Recognition
Physical Sciences →  Engineering →  Media Technology
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