3,089 research outputs found
Curved Gabor Filters for Fingerprint Image Enhancement
Gabor filters play an important role in many application areas for the
enhancement of various types of images and the extraction of Gabor features.
For the purpose of enhancing curved structures in noisy images, we introduce
curved Gabor filters which locally adapt their shape to the direction of flow.
These curved Gabor filters enable the choice of filter parameters which
increase the smoothing power without creating artifacts in the enhanced image.
In this paper, curved Gabor filters are applied to the curved ridge and valley
structure of low-quality fingerprint images. First, we combine two orientation
field estimation methods in order to obtain a more robust estimation for very
noisy images. Next, curved regions are constructed by following the respective
local orientation and they are used for estimating the local ridge frequency.
Lastly, curved Gabor filters are defined based on curved regions and they are
applied for the enhancement of low-quality fingerprint images. Experimental
results on the FVC2004 databases show improvements of this approach in
comparison to state-of-the-art enhancement methods
Directional Global Three-part Image Decomposition
We consider the task of image decomposition and we introduce a new model
coined directional global three-part decomposition (DG3PD) for solving it. As
key ingredients of the DG3PD model, we introduce a discrete multi-directional
total variation norm and a discrete multi-directional G-norm. Using these novel
norms, the proposed discrete DG3PD model can decompose an image into two parts
or into three parts. Existing models for image decomposition by Vese and Osher,
by Aujol and Chambolle, by Starck et al., and by Thai and Gottschlich are
included as special cases in the new model. Decomposition of an image by DG3PD
results in a cartoon image, a texture image and a residual image. Advantages of
the DG3PD model over existing ones lie in the properties enforced on the
cartoon and texture images. The geometric objects in the cartoon image have a
very smooth surface and sharp edges. The texture image yields oscillating
patterns on a defined scale which is both smooth and sparse. Moreover, the
DG3PD method achieves the goal of perfect reconstruction by summation of all
components better than the other considered methods. Relevant applications of
DG3PD are a novel way of image compression as well as feature extraction for
applications such as latent fingerprint processing and optical character
recognition
Modeling the growth of fingerprints improves matching for adolescents
We study the effect of growth on the fingerprints of adolescents, based on
which we suggest a simple method to adjust for growth when trying to recover a
juvenile's fingerprint in a database years later. Based on longitudinal data
sets in juveniles' criminal records, we show that growth essentially leads to
an isotropic rescaling, so that we can use the strong correlation between
growth in stature and limbs to model the growth of fingerprints proportional to
stature growth as documented in growth charts. The proposed rescaling leads to
a 72% reduction of the distances between corresponding minutiae for the data
set analyzed. These findings were corroborated by several verification tests.
In an identification test on a database containing 3.25 million right index
fingers at the Federal Criminal Police Office of Germany, the identification
error rate of 20.8% was reduced to 2.1% by rescaling. The presented method is
of striking simplicity and can easily be integrated into existing automated
fingerprint identification systems
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