Signify: Signature Verification Technique using Convolutional Neural Network
Alexandra Mae C. Laylo1, Mark Daryl A. Decillo2, Louie Andrew F. Boo3, Jeffrey S. Sarmiento4
1Alexandra Mae C. Laylo, Department of BS Computer Engineering graduate, Batangas State University, Batangas City, Philippines.
2Mark Daryl A. Decillo, Department of BS Computer Engineering graduate, Batangas State University, Batangas City, Philippines.
3Louie Andrew F. Boo, Department of BS Computer Engineering graduate, Batangas State University, Batangas City, Philippines.
4Jeffrey S. Sarmiento, Department of Computer Engineering, College of Engineering, Architecture and Fine Arts, Batangas State Univeristy, Batangas City, Philippines.
Manuscript received on 13 March 2019 | Revised Manuscript received on 17 March 2019 | Manuscript published on 30 July 2019 | PP: 1763-1767 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1015078219/19©BEIESP | DOI: 10.35940/ijrte.B1015.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Index Terms: Convolutional Neural Network, Inception, Signature Verification, Transfer Learning
Scope of the Article: E-Learning