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works well only in presence of an high resolution
sensor (1000dpi, while the common commercial sen-
sors present a resolution of about 500dpi) (Coli et al.,
2007).
An interesting texture-based approach using a sin-
gle fingerprint image was proposed by (Nikam and
Agarwal, 2009). They analyzed liveness of a fin-
gerprint image by using the gray level associated to
the fingerprint pixels. The gray level distribution in
a fingerprint image changes when the physical struc-
ture changes. Then, real and fake fingerprint images
are expected to present different textural properties.
In fact, due to the presence of sweat pores and the
perspiration phenomenon, authentic fingerprints ex-
hibit non-uniformityof gray levels along ridges, while
due to the characteristics of artificial material surface,
such as gelatin or silicon, spooffingers show high uni-
formity of gray levels along ridges.
In (Abhyankar and Schuckers, 2006), Abhyankar
and Schuckers proposed an approach based on multi-
resolution texture analysis and the inter-ridge fre-
quency analysis of fingerprint images. They used
different texture features to quantify how the gray
level distribution in a fingerprint image changes when
the physical structure changes. First order statistics
model the gray level distribution of the single pix-
els by using histograms, while second order statis-
tics refer to the joint gray level function between pair
of pixels. Two secondary features were used, Clus-
ter Shade and Cluster Prominence, based on the co-
occurrence matrix. All these features have been com-
bined with features derived from fingerprint local-
ridge frequency analysis.
Secondly, we describe a static method which com-
bines characteristics describing the morphologyof the
fingerprint and characteristics describing the perspira-
tion phenomenon (Marasco and Sansone, 2010). The
approach relies on static features derived from the vi-
sual texture of the fingerprint image. In particular,
first order statistics and residual noise standard de-
viation are exploited as morphology-based features,
while ratios between gray level values and individual
pore spacing are are exploited as perspiration-based
features.
The standard deviation of the residual noise mea-
sures the coarseness of the fingerprint image. Ma-
terials used to make fake fingers such as silicon or
gelatin consist of organic molecules which tend to ag-
glomerate, thus the surface of a fake finger is gen-
erally coarser than a live one (Moon et al., 2005).
The residual noise indicates the difference between
the original and the de-noised image, in which the
noise components are due to the coarseness of the
fake finger surface (Abhyankar and Schuckers, 2006).
In fact, according to the approach proposed by Moon
et al.(Moon et al., 2005), the surface coarseness has
been treated as a kind of gaussian white noise added
to the image.
First order statistics measure the likelihood of ob-
serving a gray value at a randomly-chosen location
in the image. The gray level associated to each pixel
is exploited to determine a vitality degree of the fin-
gerprint image. They can be computed from the his-
togram of pixel intensities in the image. The goal is
to quantify the variations of the gray level distribution
when the physical structure changes. The distinction
between a fake and a live finger is based on the differ-
ence of these statistics.
Individual pore spacing characteristics are ex-
tracted after analyzing the occurrence of pores that
causes a gray value variability in the fingerprint im-
age. In (Marasco and Sansone, 2010), according to
the algorithm proposed in (Derakhshani et al., 2003),
the 2-dimensional fingerprint image was mapped to
1-dimensional signal which represents the gray-level
values along the ridges. The gray-level variations in
the signal correspond to variations in moisture due
to the pores and the presence of perspiration. By
transforming the signal in the Fourier domain lets to
measure this static variability in gray-level along the
ridges. In particular, the focus is on frequencies corre-
sponding to the spacial frequencies of the pores. The
FFT was computed and the total energy associated to
the spacial frequency of the pores was obtained as
static feature. The coefficients of interest are from
11 to 33, since these values correspond to the spacial
frequencies (0.4 - 1.2 mm) of pores.
Intensity-based features are based on the assump-
tion that, the spoof and cadaver fingerprints images
are distributed in the dark (<150), among the 256 dif-
ferent possible intensities (Tan and Schuckers, 2005).
They have computed two particular features: i) gray
level 1 ratio, corresponding to the ratio between the
number of pixels having a gray level belonging to the
range (150, 253) and the number of pixels having a
gray level belonging to the range (1, 149); ii) gray
level 2 ratio, corresponding to the ratio between the
number of pixels having a gray level belonging to the
range (246, 256) and the number of pixels having a
gray level belonging to the range (1, 245). More-
over, they have analyzed the uniformity of gray lev-
els along ridge lines and the contrast between valleys
and ridges. Real fingerprints exhibit non-uniformity
of gray levels and high ridge/valley contrast values.
Then, the general variation in gray-level values of in
a spoof fingerprint is less than a live one. To capture
this information the gradient of the gray-level matrix
of the image have been computed, too.
ON THE ROBUSTNESS OF FINGERPRINT LIVENESS DETECTION ALGORITHMS AGAINST NEW MATERIALS
USED FOR SPOOFING
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