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All Journal Bulletin of Electrical Engineering and Informatics
Saadoon Awad Mohammed Al-Sumaidaee
Mustansiriyah University
Electrical & Electronics Engineering

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Classifications of signatures by radial basis neural network Musab Tahseen Salahaldeen Al-Kaltakchi; Saadoon Awad Mohammed Al-Sumaidaee; Raid Rafi Omar Al-Nima
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.3931

Abstract

The personal signature can be considered one of the most common behavioral biometrics. In this study, signatures are classified according to their specifications. The statistical calculation is considered for the specifications of each signature. Then, a radial basis neural network (RBNN) is adapted to apply multiple classifications for the employed signatures. A big number of signatures are utilized; they are obtained from the database called biometric ideal test (BIT). The total number of collected signatures is equally divided between the testing and training phases, where it is partitioned into 50% for the training and 50% for the testing. The proposed technique could achieve attractive performance, where each of the mean square error (MSE) and mean absolute error (MAE) attained a small value of 0.028. In addition, the proposed approach using the RBNN is compared with the different neural networks of the state-of-the-art techniques in order to demonstrate that the outcomes are acceptable and successful.
Co-Authors Musab Tahseen Salahaldeen Al-Kaltakchi Raid Rafi Omar Al-Nima