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Author Srinivasan, T. ♦ Shivashankar, S. ♦ Rakesh, A.V.B.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2006
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Shape ♦ Neurons ♦ Fingerprint recognition ♦ LVQ2 ♦ Educational institutions ♦ Directional image ♦ principal component ♦ analysis ♦ Computer science ♦ SOM ♦ Poincare index. ♦ Image matching ♦ Neural networks ♦ Pattern classification ♦ Gabor filters ♦ Principal component analysis
Abstract In this paper, we present a novel adaptively automated fingerprint classification scheme, which is computationally efficient and resolves both intra-class diversities and inter-class similarities. Initially, preprocessing of fingerprint images is carried out to enhance the image. For classification based on global shape, directional image is computed. Principal component analysis is employed in first stage for dimensionality reduction and to get feature space that accounts for as much of the total variation as possible. In second stage, self-organizing maps are involved for further dimension reduction and data clustering. We use the Kohonen topological map for pattern classification. The learning process takes into account the large intra class diversity and the continuum of fingerprint pattern types. Finally LVQ2 maps the class separated fingerprint images into their respective class, the winner and runner-up neuron are trained in such a way that they take into account the inter-class similarities. Experimental results show that AAFFC achieves an accuracy of around 89 % for five-class classification tested on NIST 4 without rejection
Description Author affiliation: Dept. Of Comput. Sci. & Eng., Sri Venkateswara Coll. of Eng., Sriperumbudur (Srinivasan, T.)
ISBN 0769525288
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2006-10-16
Publisher Place China
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 676.07 kB
Page Count 6
Starting Page 72
Ending Page 77

Source: IEEE Xplore Digital Library