ABBAS, NIDAA HASAN and YASEN, KHALED N and ALI FARAJ, KAMARAN HAMA and RAZAK, LWAY FAISAL and ALLAH, FAHAD LAYTH (2018) Offline Handwritten Signature Recognition Using Histogram Orientation Gradient and Support Vector Machine. Journal of Theoretical and Applied Information Technology, 96 (8). pp. 2075-2084. ISSN 1817-3195
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Abstract
Human being authentication by offline handwritten signature biometric research has been increasing, especially in the last decade. Verification process of an offline handwritten signature is not trivial task, because an individual rarely signs exactly the same signature whenever he/she signs, which is referred to as intra-user variability. The objective of this paper is proposing a feature vector of an offline handwritten signature by using an efficient algorithm as a strong feature extraction namely Histogram Orientation Gradient (HOG), in order to be passed into Support Vector Machine (SVM) classifier for the recognition operation. An experiment has been conducted to estimate the accuracy and performance of the proposed algorithm by using SIGMA database, which has more than 6,000 genuine and 2,000 forged signature samples taken from 200 individuals. The result has given accuracy as 96.8% as successful rate coming from the error type as: False Accept Rate (FAR) is 3% and False Reject Rate (FRR) is 3.35%.
Item Type: | Article |
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Uncontrolled Keywords: | Offline Signature, Biometrics, verification, Histogram Oriented Gradient (HOG), Support Vector Machine (SVM). |
Subjects: | H Social Sciences > HA Statistics |
Divisions: | Department of Accounting > Research papers |
Depositing User: | ePrints Depositor |
Date Deposited: | 06 Oct 2024 12:02 |
Last Modified: | 06 Oct 2024 12:02 |
URI: | https://eprints.cihanuniversity.edu.iq/id/eprint/1395 |