Kernel Based K-Nearest Neighbor Method to Enhance the Performance and Accuracy of Online Signature Recognition
R. Ravi Chakravarthi1, E. Chandra2

1R. Ravi Chakravarthi, Masters of Philosophy degree in Computer Science from Manonmaniam Sundaranar University (MSU), Tirunelveli, Tamilnadu, India.
2E. Chandra, Professor and Head, Department of Computer Science, Bharathiar University, Coimbatore, India.

Manuscript received on 04 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 985-992 | Volume-8 Issue-10, August 2019 | Retrieval Number: J91390881019/2019©BEIESP | DOI: 10.35940/ijitee.J9139.0881019
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Abstract: Signature recognition is a significant among the most fundamental biometrics recognition techniques, is a key bit of current business works out, and is considered a noninvasive and non-undermining process. For online signature recognition, numerous methods had been displayed previously. In any case, accuracy of the recognition framework is further to be enhanced and furthermore equal error rate is further to be decreased. To take care of these issues, a novel order method must be proposed. In this paper, Kernel Based k-Nearest Neighbor (K-kNN) is presented for online signature recognition. For experimental analysis, two datasets are utilized that are ICDAR Deutsche and ACT college dataset. Simulation results show that, the performance of the proposed recognition technique than that of the existing techniques in terms of accuracy and equal error rate.
Keywords: Online signature recognition, Kernel Based k-Nearest Neighbor (K-kNN), accuracy, equal error rate.
Scope of the Article: Pattern Recognition