A Novel Hybrid FLANN-PSO Technique for Real Time Fingerprint Classification.
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 featur...
| Publicado en: | Medico-Legal Update Vol. 19; no. 2; pp. 740 - 747 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Institute of Medico-legal publications Pvt Ltd
Jul-Dec2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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. |
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