Abstract—Contact-based sensors are the traditional devices
used to capture fingerprint images in commercial and homeland
security applications. Contact-less systems achieve the fingerprint
capture by vision systems avoiding that users touch any parts of
the biometric device. Typically, the finger is placed in the working
area of an optics system coupled with a CCD module. The
captured light pattern on the finger is related to the real ridges
and valleys of the user fingertip, but the obtained images present
important differences from the traditional fingerprint images.
These differences are related to multiple factors such as light,
focus, blur, and the color of the skin. Unfortunately, the identity
comparison methods designed for fingerprint images captured
with touch-based sensors do not obtain sufficient accuracy when
are directly applied to touch-less images. Recent works show
that multiple views analysis and 3D reconstruction can enhance
the final biometric accuracy of such systems. In this paper we
propose a new method for the identification of the minutiae pairs
between two views of the same finger, an important step in the
3D reconstruction of the fingerprint template. The method is
divisible in the sequent tasks: first, an image preprocessing step
is performed; second, a set of candidate minutiae pairs is selected
in the two images, then a list of candidate pairs is created;
last, a set of local features centered around the two minutiae is
produced and processed by a classifier based on a trained neural
network. The output of the system is the list of the minutiae pairs
present in the input images. Experiments show that the method
is feasible and accurate in different light conditions and setup
configurations.
Index Terms—neural-networks, touch-less fingerprint, contactless
fingerprint, minutiae matching