Learning fingerprint orientation fields using continuous restricted Boltzmann machines

Mihir Sahasrabudhe, Anoop M. Namboodiri

Abstract:

We aim to learn local orientation field patterns in
fingerprints and correct distorted field patterns in noisy fin-
gerprint images. This is formulated as a learning problem and
achieved using two continuous restricted Boltzmann machines.
The learnt orientation fields are then used in conjunction with
traditional Gabor based algorithms for fingerprint enhancement.
Orientation fields extracted by gradient-based methods
are local, and do not consider neighboring orientations. If
some amount of noise is present in a fingerprint, then these
methods perform poorly when enhancing the image, affecting
fingerprint matching. This paper presents a method to correct
the resulting noisy regions over patches of the fingerprint by
training two continuous restricted Boltzmann machines. The
continuous RBMs are trained with clean fingerprint images
and applied to overlapping patches of the input fingerprint.
Experimental results show that one can successfully restore
patches of noisy fingerprint images.