Performances Enhancement of Fingerprint Recognition System Using Classifiers
Kashif Noor,
Tariqullah Jan,
Mohammed Basheri,
Amjad Ali,
Ruhul Amin Khalil,
Mohammad Haseeb Zafar,
Majad Ashraf,
Mohammad Inayatullah Babar,
Syed Waqar Shah
Affiliations
Kashif Noor
Department of Electrical Engineering, Faculty of Electrical and Computer Systems Engineering, University of Engineering and Technology at Peshawar, Peshawar, Pakistan
Tariqullah Jan
Department of Electrical Engineering, Faculty of Electrical and Computer Systems Engineering, University of Engineering and Technology at Peshawar, Peshawar, Pakistan
Mohammed Basheri
Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Amjad Ali
Department of Electrical Engineering, University of Science and Information Technology Peshawar, Peshawar, Pakistan
Ruhul Amin Khalil
Department of Electrical Engineering, Faculty of Electrical and Computer Systems Engineering, University of Engineering and Technology at Peshawar, Peshawar, Pakistan
Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Majad Ashraf
Department of Electrical Engineering, Faculty of Electrical and Computer Systems Engineering, University of Engineering and Technology at Peshawar, Peshawar, Pakistan
Mohammad Inayatullah Babar
Department of Electrical Engineering, Faculty of Electrical and Computer Systems Engineering, University of Engineering and Technology at Peshawar, Peshawar, Pakistan
Syed Waqar Shah
Department of Electrical Engineering, Faculty of Electrical and Computer Systems Engineering, University of Engineering and Technology at Peshawar, Peshawar, Pakistan
Fingerprint recognition is best known and generally used as a biometric technology because of their high acceptability, immutability, and uniqueness. A fingerprint consists of ridges and valleys pattern also known as furrows. These patterns fully develop in the mother’s womb and remain constant throughout the whole lifetime of the individual. The ridge bifurcation and ridge termination are the main minutiae features that are extracted for identification of individuals in fingerprint recognition system. The aim of this paper is to enhance the performance of the fingerprint recognition systems using classifiers. To achieve the aim, fingerprints from the FV2002 database are used, before these fingerprints are evaluated, image enhancement and binarization is applied as a pre-processing on fingerprints, by combining many methods to build a database of fingerprint features having minutia marking and minutia feature extraction. The fingerprint recognition is presented by image classification using MATLAB classifiers, i.e., Decision Tree, Linear Discriminant Analysis, medium Gaussian support vector machine (MG-SVM), fine K-nearest neighbor, and bagged tree ensemble. The aim of this paper is to make a comparison between classifiers for performance enhancement of the fingerprint recognition system. The MG-SVM classifiers significantly give the highest verification rate of 98.90% among all classifies used.
