In this study, recognition of fingerprint images has been performed by recent classifiers as well as some important and common classifiers available in the literature. The classification methods used in the study are support vector machines, k-nearest neighbors, Naive-Bayes, decision tree learning, and deep neural networks. Training/testing data set has been obtained basically by using four different versions of fingerprint images of 165 different fingers. Additional seven rotated versions of each different fingerprint images are also used to extend the data set. Feature vector of each fingerprint image (a fingercode) has been produced by using directional Gabor filters and averaging specific regions (sectors) of their output images. After creating fingercode data set, all classifiers has been trained to recognize fingerprint images. Detailed simulation results show that deep neural networks can be effectively used among all classifiers for recognition of fingerprint images.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.