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Title: Signature recognition using binary features and KNN

Authors: Hedjaz Hezil; Rafik Djemili; Houcine Bourouba

Addresses: Faculty of Science and Technology, Department of Electronics and Telecommunications, University of May 8th 1945, Guelma, Algeria ' Faculty of Technology, Department of Electrical Engineering, Université du 20 Août 1955, Skikda, Algeria ' Faculty of Science and Technology, Department of Electronics and Telecommunications, University of May 8th 1945, Guelma, Algeria

Abstract: This paper proposes the use of binary features in offline signature recognition systems. Indeed, offline signature recognition finds mainly its importance for the authentication of administrative and official documents in which a higher accuracy is needed. In the proposed approach, features are extracted by using two descriptors: binary statistical image features (BSIF) and local binary patterns (LBP). To assess the reliability of the method, experiments were carried out using two publicly available datasets, MCYT-75 and GPDS-100 databases. Using a k-nearest neighbour classifier, recognition performances reach values high as 97.3% and 96.1% for MCYT-75 and GPDS-100 databases respectively. In signature verification, the classification accuracy measured with equal error rate (EER) achieved 4.2% and 4.8% respectively on GPDS-100 and GPDS-160. In addition, the EER for the MCYT-75 database has attained 7.78%. All those accuracies outperformed various performance results reported in literature.

Keywords: offline signature recognition; feature extraction; biometric; local binary patterns; LBP; binary statistical image features; BSIF; KNN.

DOI: 10.1504/IJBM.2018.090121

International Journal of Biometrics, 2018 Vol.10 No.1, pp.1 - 15

Available online: 20 Feb 2018 *

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