Neural networks applied to fingerprint recognition

Abstract

In this paper we use a Multi-layer perceptron neural network with learning algorithm retropropagation errors, for application in fingerprint recognition. The objective is to measure the efficiency of the neural network by varying the test data. We observe the behavior of the network in the special case of a partial print. Once the overall structure of the network was designed, tested and properly trained, we proceeded with the testing process, varying the characteristic points and their particular characteristics. Overall, the results demonstrate a stronger recognition when all the characteristic points for the individual prints are available. The recognition rate begins to decrease as the number of characteristic points is reduced to 12, but increases when the number of points is 10, 8 or 5. We obtained a good percentage of hits to remove the features that depended on the center of the footprint and the footprint of the code, in this way to reach the desired goal. © 2009 Springer Berlin Heidelberg.