Abstract—Numerous algorithms have been developed to improve the speed of fingerprint recognition and this assumes prime importance when dealing with a large database of fingerprint images. In this paper, we propose a novel technique of using the image parameters like mean, median, variance, standard deviation and root mean square value to train an adaptive network based fuzzy inference system (ANFIS) to classify the test image into one of the six popular categories of fingerprints namely arch, tented arch, left loop, right loop, whorl and twin loop. This classification allows us to proceed with the actual matching algorithm only with the images which fall in that particular category of ridge structure thereby saving time.
Index Terms—fingerprint classification, parameters, ANFIS, weights
Rajesh Kumar is with the Electrical Engineering Department, Malaviya National Institute of Technology, Jaipur, 302017, INDIA (corresponding author phone: 91-9413301134; fax: 91-141-2529029)
B R Deva Vikram was with MNIT, Jaipur, 302017, INDIA. He is now a graduate student in the Department of Electrical Engineering in Texas A&M University USA.
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Cite: B R Deva Vikram and Rajesh Kumar, Senior Member, IAENG, "A Novel Method of Fingerprint Classification Using Image Parameters on ANFIS,"
International Journal of Computer Theory and Engineering vol. 1, no. 4, pp. 352-357, 2009.
