NDLI: Fingerprint classification based on a Q-Gaussian multiclass support vector machine

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Fingerprint classification based on a Q-Gaussian multiclass support vector machine

Content Provider	ACM Digital Library
Author	Wang, Kuanquan Hammad, Mohamed
Abstract	Accurate recognition and actual classification of fingerprint are vital and necessary for fingerprint identification. Previous researchers have used many classification algorithms to develop fingerprint classification model, but they still have some certain problems like time of implementation to do the task, cost of implementation, working on non-linear features, working on multi-dimensional features and under or over learning problems. In this paper, a Q-Gaussian multi-class support vector machine (QG-MSVM) for fingerprint classification is proposed in which Q-Gaussian function is incorporated into SVM as a kernel function. The proposed method is tested in CASIA, FVC2000, FVC2002 and FVC2004 databases and compared with the MSVM methods with linear kernel, Gaussian Radial Basis Function kernel (RBF), Polynomial kernel and other state-of-the-art methods. The experimental results show that QG-MSVM demonstrates better performance than other classifiers and overcome many MSVM problems. The overall performance of the QG-MSVM classifier is comprehensively superior to all others.
Starting Page	39
Ending Page	44
Page Count	6
File Format	PDF
ISBN	9781450348713
DOI	10.1145/3077829.3077836
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-04-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Fingerprint identification Rbf Qg-msvm Fingerprint classification
Content Type	Text
Resource Type	Article

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