A Novel Hybrid FLANN-PsO Technique for real Time Fingerprint Classification Mishra Annapurna1, Dehuri Sachidananda2 1Dept. of Electronics and Communication Engineering, Silicon Institute of Technology, Silicon Hills, Patia, Bhubaneswar, Odisha, India 2Dept. of Information and Communication Technology, Fakir Mohan University, VyasaVihar, Balasore, Odisha, India Online published on 8 August, 2019. 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. Top Keywords Particle swarm optimization, angular features, parameters, Henry system. Top |