REVISTA DYNA NEW TECHNOLOGIES

Key words:

Huella dactilar, Red neuronal artificial SOM, ruido, fingerprint, Artificial Neural network SOM, noise.

Article type:

ARTICULO DE INVESTIGACION / RESEARCH ARTICLE

Section:

RESEARCH ARTICLES

The objective of this article is to classify the fingerprints using the artificial neural network SOM (self-organizing map) of Teuvo Kohonen, and as a means of learning the network the orientations of the lines marked by the ridges and valleys using the Gabor filter were used, despite having present noise. Know and apply the highlight of the SOM network in the learning of scanned images of each type of footprint. For the learning process, 100 fingerprint images of different types were used (25 type ARCO, 25 type LEFT LOOP, 25 type RIGHT LOOP and 25 type WHORL)
obtained from a public database. In the mapping, 2 groups of images were used, the first being 10 new images for each type (bone 40 tracks) and a second group that are the same tracks of the previous group, but adding "salt and pepper" noise with density 0.02. Obtaining encouraging results for the first group 100% correct identification of types of tracks and the second group with 95% correct identification. It is concluded that the SOM network allows to identify types of fingerprints and with noise apply additional filters to obtain the same percentage.