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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    Title :

    A Finger Knuckle Print Classification System Using SVM for Different LBP Variants


    Additional title:

    Lect. Notes Electrical Eng.



    Conference:

    International Conference on Robotics, Vision, Signal Processing and Power Applications ; 2021 April 05, 2021 - April 06, 2021



    Publication date :

    2024-03-31


    Size :

    8 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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