Abstract
Finger knuckle print is one of the most important biometric traits and plays a vital role in a secure identification system. In this paper, performance evaluation of local binary pattern (LBP) and its variants center symmetric local binary pattern (CS-LBP) and median local binary pattern (MLBP) are investigated. After feature extraction, a support vector machine (SVM) with the linear kernel is used for the performance evaluation of two different datasets named the Poly-U FKP dataset and the USM-FKP dataset. The experimental results show that CS-LBP performs better for the USM-FKP dataset with an accuracy of 86.2% which demonstrates the potential of the FKP classification system.
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Acknowledgements
Acknowledgment to Ministry of Higher Education Malaysia for Fundamental Research Grant Scheme with Project Code: FRGS/1/2021/ICT02/USM/02/1 for the financial support of this research. The images used in this study are acquired through approval ethical protocol with the study protocol code USM/JEPeM/21100657.
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Riaz, I., Ali, A.N., Huqqani, I.A. (2024). A Finger Knuckle Print Classification System Using SVM for Different LBP Variants. In: Ahmad, N.S., Mohamad-Saleh, J., Teh, J. (eds) Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. RoViSP 2021. Lecture Notes in Electrical Engineering, vol 1123. Springer, Singapore. https://doi.org/10.1007/978-981-99-9005-4_71
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DOI: https://doi.org/10.1007/978-981-99-9005-4_71
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