Fingerprint classification by SPCNN and combined LVQ networks

Authors:
Luping Ji;Zhang Yi;Xiaorong Pu
Affiliations:
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

Citing 4
Cited 1

A Multichannel Approach to Fingerprint Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprints classification using artificial neural networks: a combined structural and statistical approach

Neural Networks
On learning to estimate the block directional image of a fingerprint using a hierarchical neural network

Neural Networks
PCNN models and applications

IEEE Transactions on Neural Networks

Review article: Review of pulse-coupled neural networks

Image and Vision Computing

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Abstract

This paper proposes a novel fingerprint classification method. It uses an SPCNN (Simplified Pulse Coupled Neural Network) to estimate directional image of fingerprint, and quantizes them to obtain fingerprint vector. Then, a fully trained LVQ (Learning Vector Quantization) neural network is used as classifier for the fingerprint vector to determine the corresponding fingerprint classification. Experiments show this proposed method is robust and has high classification accuracy.