A Novel Hybrid FLANN-PSO Technique for Real Time Fingerprint Classification

Abstract

In this paper we are presenting a Particle swarm optimized functional link neural network for classifying a
collection of real time fingerprints in the field of biometric recognition. From the collected fingerprints the
feature vectors are extracted as a collection of different angle oriented features using the Gabor filter bank.
The classes of the fingerprints are assigned as per the Henry System. For classification a novel FLANN-PSO
algorithm is used and tested for accuracy through different parameters and different angular features of the
fingerprints. In this work we have obtained an accuracy of 98% for real time collected fingerprint images. It
has been compared with other classifiers and the results obtained of this work in terms of accuracy and MSE
value has shown appreciable improvement over the other algorithms.