A filterbank-based representation for classification and matching of fingerprints
We view fingerprints as oriented texture patterns, textures which exhibit an inherent and well-defined sense of directionality. Given a fingerprint image, we demonstrate that reliable translation and rotation invariant representations can be built based entirely on the inherent properties of the underlying fingerprint texture. We also illustrate that the representations thus derived are useful for robust discrimination of the fingerprints. In particular, we present the application of our novel representation scheme for solving the problems of fingerprint classification and matching on large datasets of fingerprint images acquired in real situations. The proposed scheme of generic representation for oriented textures relies on extracting one or more invariant frames of reference of the oriented texture based on an analysis of its orientation field. The fingerprint classification is based on a two-stage classifier which uses a K-NN classifier in its first stage and a set of neural network classifiers in its second stage.