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César G Pachón-Suescún
Nueva Granada Military University
Computer Science & IT Electrical & Electronics Engineering

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Offline signature verification using DAG-CNN Javier O. Pinzón-Arenas; Robinson Jiménez-Moreno; César G Pachón-Suescún
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (641.031 KB) | DOI: 10.11591/ijece.v9i4.pp3314-3322

Abstract

This paper presents the implementation of a DAG-CNN which aims to classify and verify the authenticity of the offline signatures of 3 users, using the writer-independent method. In order to develop this work, 2 databases (training / validation and testing) were built manually, i.e. the manual collection of the signatures of the 3 users as well as forged signatures made by people not belonging to the base and altered by the same users were done, and signatures of another 115 people were used to create the category of non-members. Once the network is trained, its validation and subsequent testing is performed, obtaining overall accuracies of 99.4% and 99.3%, respectively, showing the features learned by the network and verifying the ability of this configuration of neural network to be used in applications for identification and verification of offline signatures.
Co-Authors Javier O. Pinzón-Arenas Robinson Jiménez-Moreno