A Signature Verification System with Ensemble Classifier
Alpana Deka*, department of Computer Science, NERIM Group of Institutions, Guwahati, India.
Manuscript received on November 6, 2019. | Revised Manuscript received on November 20, 2019. | Manuscript published on 30 November, 2019. | PP: 4132-4136 | Volume-8 Issue-4, November 2019. | Retrieval Number: C5445098319/2019©BEIESP | DOI: 10.35940/ijrte.C5445.118419
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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: Handwritten signature is considered as one of the established authentication process to study the behavioral nature of a person. This paper focuses on verification of offline handwritten signatures (for English scripts) as either genuine or forgery. Here the considered samples are genuine, skilled and simple forgeries. The verification is carried out by ensembling the three base classifiers Naive Bayes (NB), K-Nearest Neighbor (KNN) and Kmeans classifiers. The accuracies for skilled and simple forgeries are obtained as 86 % and 92 % respectively.
Keywords: Ensemble Classifier, Features extraction, Offline, Preprocessing.
Scope of the Article: Big Data Analytics Application Systems.