EXCLUDING THE REMAINING RIDGES OF FINGERPRINT
IMAGE
En Zhu, Jianping Yin, Chunfeng Hu, Guomin Zhang
School of Computer Science, National University of Defense Technology, Changsha 410073, China
Jianming Zhang
Department of Computer Science, Hunan City University, Yiyang 413049, China
College of Computer and Communication, Hunan University, Changsha 410082, China
Keywords: Fingerprint segmentation, Recoverable, Remaining ridges.
Abstract: Fingerprint segmentation is usually to identify non-ridge regions and unrecoverable low quality ridge
regions and exclude them as background so as to reduce the time of image processing and avoid detecting
false features. In ridge regions, including high quality and low quality, there are often some remaining
ridges which are the afterimage of the previously scanned finger and are expected to be excluded from the
foreground. However, existing segmentation methods do not take the case into consideration, and often, the
remaining ridge regions are falsely taken as foreground. This paper proposes two steps for fingerprint
segmentation aiming to exclude the remaining ridge region from the foreground. The non-ridge regions and
unrecoverable low quality ridge regions are removed as background in the first step, and then the
foreground produced by the first step is further analyzed so as to remove the remaining ridge region. The
proposed method turns out effective in avoiding detecting false ridges and in improving minutiae detection.
1 INTRODUCTION
Fingerprint segmentation is an important problem in
fingerprint recognition. A fingerprint image usually
has to be segmented to remove uninterested regions
before some other steps such as enhancement and
minutiae detection so that the image processing will
consume less CPU time. A fingerprint image
generally consists of different regions: non ridge
region, high quality ridge region, and low quality
ridge region. Fingerprint segmentation is usually to
identify non-ridge regions and unrecoverable low
quality ridge regions and exclude them as
background so as to reduce the time of image
processing and avoid detecting false features and
further to improve the recognition accuracy. Most
segmentation methods are block-wised ones (Mehtre,
1987; Mehtre, 1986; Mehtre, 1989; Mehtre, 1993;
Ratha, 1995; Hong, 1998; Klein, 2002) which divide
the fingerprint image into un-overlapped blocks and
decide on the type (background and foreground) of
each block. And some other methods are pixel-wised
ones (Bazen 2000, Bazen 2001) which determine the
type of each pixel. Fingerprint segmentation
typically computes the feature (or feature vector) of
each element, block or pixel, and then determine the
element’s type based on the feature (vector). The
features used in fingerprint segmentation mainly
include statistical features of pixel intensity,
directional image and ridge projection signal, etc.
Methre (Mehtre, 1987; Mehtre, 1986; Mehtre, 1989;
Mehtre 1993) uses gray variance and the histogram
of pixel gradients in a sub-image block for
segmentation. For each sub-image block Ratha
(Ratha, 1995) computes the variance of the
projection signal on different directions. The
foreground block is of large variance along the
direction orthogonal to the ridges and is of small
variance along the direction parallel to the ridges.
And background is usually of small variance along
all directions. Hong (Hong, 1998) uses the features,
including frequency, variance and the average
difference between the peaks and valleys, of the
ridge projection signal along the direction
orthogonal to the local ridges for segmentation.
Klein (Klein, 2002) computes gray mean, variance,
346
Zhu E., Yin J., Hu C., Zhang G. and Zhang J. (2006).
EXCLUDING THE REMAINING RIDGES OF FINGERPRINT IMAGE.
In Proceedings of the First International Conference on Computer Vision Theory and Applications, pages 346-353
DOI: 10.5220/0001360303460353
Copyright
c
SciTePress
gradient consistency and Gabor response for
segmentation by using HMM. Bazen (Bazen, 2000;
Bazen, 2001) computes gray mean (Bazen, 2001),
variance (Bazen, 2001) and gradient coherence
(Bazen, 2000; Bazen, 2001) to pixel-wisely segment
fingerprint image. Yin (Yin, 2005) also uses
coherence, mean and variance. Jain (Jain, 1997) uses
the output of a set of Gabor filters for segmentation
by adopting clustering. Wang (Wang, 2004) uses
Gaussian-Hermite Moments. Ong (Ong 2003) uses
the orientation coherence for coarse segmentation
and then refine the results by Fourier-based
enhancement, adaptive thresholding, and
postprocessing. And Ren (Ren, 2003) detects feature
dots which are somewhat like ridge edge points to
segment fingerprint image.
Most existing segmentation methods aim to and
are able to exclude regions containing no ridges (e.g.
Fig.1 (d)) or of low quality and hence unrecoverable
(e.g. Fig.1 (c)). Yet none of these methods considers
the excluding of the remaining ridges (e.g. Fig.1
(e)), the afterimage of the previously scanned finger.
And consequently, the remaining ridges are often
falsely taken as the foreground in the case that they
are of clear or recoverable ridge structure. Another
problem in fingerprint segmentation is how to know
whether a low quality ridge block is recoverable or
unrecoverable so as to guide the segmentation. The
typical solution is to visually decide the types,
recoverable and unrecoverable, and feed some
samples, whose type are visually decided, into a
classifier at its training stage, and the trained
classifier would be used to classify the fingerprint
regions. However, in fingerprint image processing,
the process of ridge recovering is done by a certain
algorithm not by manual, and therefore a manually
recoverable ridge block maybe unrecoverable for the
specific algorithm since the algorithm can not be
cleverer than the human brain. Recovering low
quality ridges, e.g. enhancement using a texture
filter by tuning its orientation and frequency (Hong,
1998; Ailisto, 15; Zhu, 2004), usually depends on
the correct computation of ridge orientation.
Incorrect computation of the ridge orientation means
that the ridge can not be recovered. Thus we propose
to segment the fingerprint image through two steps:
The first segments according to the results of ridge
orientation estimation. The recoverable ridge
regions, including high quality and low quality, with
their orientations correctly estimated, are identified
as foreground in the first step. The foreground
identified by the first step may contain remaining
ridge region, and the second step further excludes
the remaining ridge region from the foreground. In
the following sections which will describe the
proposed algorithm in detail, we call the first step
primary segmentation, and the second step
secondary segmentation. Section 2 describes the
primary segmentation. Section 3 describes the
secondary segmentation. Section 4 contains the
experimental results. Section 5 is the conclusion.
And section 6 is the acknowledgement.
(e)
(b)
(c)
(d)
(a)
Figure: 1 Fingerprint regions: (a) high quality ridge
region, (b) recoverable low quality region (the ridge
interrupts are recoverable in this case), (c) unrecoverable
low quality region, (d) non ridge region, (d) remaining
ridge region.
2 PRIMARY SEGMENTATION
Fingerprint segmentation serves for decreasing the
computational expenditure at image processing and
for improving the accuracy of feature (typically
minutiae) extraction, because excluding non-ridge
regions and unrecoverable ridge regions helps to
reduce CPU consumption and avoid introducing
false minutiae, and keeping recoverable ridge
regions not removed helps to avoid losing true
minutiae. However, recoverable ridges are often
actually not recovered in the enhance image,
because they are just manually and not
algorithmically recoverable mainly due to that their
orientations are not correctly estimated. Fig.2 (a) and
(b) show an example of taking algorithmically
unrecoverable ridges, due to the incorrect estimation
of ridge orientation, as foreground. Besides, it is
hard to decide the recoverability of low quality
ridges, and consequently, recoverable ridges, in spite
of the correct estimation of ridge orientations, are
often taken as background. Fig.2 (c) and (d) show an
example of take manually recoverable ridges as
background. Fig.2 (b) and (d) are the segmentation
EXCLUDING THE REMAINING RIDGES OF FINGERPRINT IMAGE
347
(a) (b) (c) (d)
Figure 2: Examples of fingerprint image segmentation by VeriFinger4.2 of Neuro. (a) and (c) are the original images
collected using SecuGen device, and (b) and (d) are the segmented results, respectively.
results by VeriFinger4.2 published by
Neurotechnologija (hereinafter abbreviated as
Neuro) (Neurotechnologija Ltd., 2004) which have
participated in FVC2002 (Maio, 2002) and
FVC2004 (Maio, 2004) and came top in both the
two contests. The main difficulty in fingerprint
segmentation is to answer whether the (low quality)
ridge block is recoverable or unrecoverable by an
automatic algorithm. A well trained classifier may
be able to distinguish high quality ridges and
manually recoverable low quality ridges from other
type of regions. In the case of ridge orientation
estimation following the image segmentation,
although all the foreground blocks are of high
quality or manually recoverable, none of the ridge
orientation estimation algorithms can ensure the
orientation of each foreground block would be
correctly computed, and as a result, those blocks
with their orientations falsely estimated are
practically not recoverable for the recovering
algorithm, such as enhancement (Hong, 1998;
Ailisto, 2003; Zhu, 2004) and ridge tracing (Maio,
1997; Jiang, 2001). Thus, it is reasonable that the
ridge orientation estimation proceeds prior to the
segmentation and that the blocks of falsely estimated
orientation should be taken as background.
The proposed primary segmentation is based on
the work of (Zhu, 2005). (Zhu, 2005) Proposed a
method to estimate the fingerprint image quality by
training a neural network which responds to correct
ridge orientation of ridge block (of high quality or
manually recoverable) with a large value, and
responds with a small value to those blocks which
contain no ridges or contain manually unrecoverable
ridges or are of falsely estimated orientations. For
each image block, a feature vector
1121
,...,, CCC
is
computed to be fed into the network which will
respond to the vector with a value. The responded
value by the trained network to a specific block is
depended on the orientation, since the items from C5
to C11 of the input vector
1121
,...,, CCC
(Zhu,
2005) have a close relationship with the estimated
ridge orientation. Suppose that the image is divided
into non-overlapped blocks like in (Zhu, 2005), and
let W(i,j) denote the block at the ith row and the jth
column. And the ridge orientation is quantified into
16 orientations: the kth orientation is
/16k
π
(0 16)k
<
. For each block W(i,j), 16 vectors,
denoted as
12 11
, ,...,
k
CC C
(0 16)k≤<
, can be
computed,
12 11
, ,...,
k
CC C
corresponding to the
orientation
/16k
π
. For each block, feed the 16
vectors to the network and obtain 16 responded
values, respectively. The trained network would
generally respond with large values to the vectors
corresponding to the orientation close to the true
ridge orientation, and respond with small values to
other vectors. Let
[](,)
R
kij be the responded
value to the kth vector of the block W(i,j). We use
these responded values to each block to estimate the
ridge orientation and primarily segment the image
(primary segmentation) as follows.
=
+=
1
1
),]([)(),](['
u
jiukRujikR
ω
(1)
∑∑
=−=
++=
1
1
1
1
),](['),(),](["
uv
vjuikRvujikR
ϖ
(2)
(
)
"[ ]( , ) max "[ ]( , ) 0 15R l ij R k ij k=≤
(3)
((,)) 16OW i j l
π
(4)
1"[](,)
((,))
0"[](,)
m
m
R
lij t
MWi j
R
lij t
=
<
(5)
VISAPP 2006 - IMAGE ANALYSIS
348
(a) (b) (c)
Figure 3: Segmentation failing to remove remaining ridges. (a) original image FVC2004_DB2_35_4, (b) result by Neuro,
(c) result by the primary segmentation.
where
()u
ω
and
(,)uv
ϖ
are Gaussian filter and
are used to smooth noisy responded values.
((,))OW i j is the estimated ridge orientation.
((,))
M
Wij
denotes the result of the primary
segmentation:
(, )Wij is a foreground block if
((,))
M
Wij =1, and background if
((,))
M
Wij
=0.
3 SECONDARY SEGMENTATION
The primary segmentation identifies and removes
non-ridge blocks and unrecoverable ridge blocks
(manually unrecoverable or having the falsely
estimated orientations and thus algorithmically
unrecoverable). The foreground of the primary
segmentation contains ridge block of correct
orientation. The remaining ridges of the fingerprint
image tend to be included in foreground, if they
have recoverable clear ridge structure and have their
orientation correctly estimated. It is difficult to
identify remaining ridges by once segmentation,
including the existing segmentation methods and the
proposed primary segmentation as in Fig.3 which
shows the example of segmentation by existing
method and the propose primary segmentation
which fail to remove the remaining ridges, since
remaining ridges often have clear structures and
since it is possible, for two fingerprints A and B as
in Fig.4, the remaining ridges of fingerprint A have
the similar features with or even appear clearer than
the true ridges of fingerprint B. Fortunately, within
the same image, there are typical differences
between the remaining ridges and the true ridges: (1)
the average gray value of the remaining ridge block
is generally bigger than that of the true ridge block;
(2) the difference between ridge and valley in the
remaining ridge block is smaller than in the true
ridge block. The two differences are used by the
secondary segmentation to further identify and
remove the remaining ridges.
(a) (b)
Fig. 4 Remaining ridges and true ridges from two
fingerprints have possible similar features. (a) Fingerprint
A, the left part contains remaining ridges. (b) Fingerprint
B, which contains true ridges which are of similar
features, such as gray value and inter-ridge-valley
difference, with the remaining ridges of Fingerprint A.
Let the LG(W) be the local average gray value of
the block W. The global average gray value of all
the foreground blocks would be
((,))1
(, )
((,))
((,))
MWij
Wij
LG W i j
MG
MWi j
=
=
(6)
And let LA(W) be the local inter-ridge-valley gray
difference of the block W. The global average inter-
ridge-valley gray difference of all the foreground
blocks is computed as
((,))1
(, )
((,))
((,))
MWij
Wij
L
AW i j
MA
MWi j
=
=
(7)
The first difference between the remaining ridge
block and the true ridge block from the same image
EXCLUDING THE REMAINING RIDGES OF FINGERPRINT IMAGE
349
can be described by LG and MG: The value of
L
GMG is usually bigger at the remaining ridge
block than at the true ridge block. And the second
difference can be described using LA and MA: The
value of
LA MA
is usually smaller at the
remaining ridge block than at the true ridge block.
Some blocks which have small LG value and large
LA value can be taken as the true ridge blocks
without regarding to the value of
L
GMG and
LA MA
, and similarly, those blocks which have
large LG value and small LA value can be taken as
the remaining ridge blocks without considering the
value of
L
GMG and
L
AMA . Therefore,
the secondary segmentation uses LGMGLA
MA to reclassify the foreground blocks of the
primary segmentation. For the blocks from the same
image, they have the same MG value and same MA
value. LMS modal (Press, 1992) is used for the
secondary segmentation. Suppose that N samples,
including positive samples and negative samples, are
selected for training the classifier and are denoted as
{}
NiWyWMAWLAWMGWLG
iiiii
1)(),(),(),(),(
:
1)( =
i
Wy if
i
W is a positive sample (true ridge
block),
1)( =
i
Wy if
i
W is a negative sample
(remaining ridge block). The LMS modal is
described by equation (8).
=
)(
...
)(
)(
)(
)()()()(1
...............
)()()()(1
)()()()(1
)()()()(1
3
2
1
4
3
2
1
0
3333
2222
1111
NNNNN
Wy
Wy
Wy
Wy
a
a
a
a
a
WMAWLAWMGWLG
WMAWLAWMGWLG
WMAWLAWMGWLG
WMAWLAWMGWLG
(8)
where
43210
,,,, aaaaa
are the parameters to be
solved. At the secondary segmentation, given a
block
W , compute
ˆ
()yW
as equation (9).
01 2
34
ˆ
() () ()
() ()
yW a a LGW a MGW
a LAW a MAW
=+ +
+⋅ +
(9)
If
ˆ
(( )) 1sign y W =−
where W is a foreground
block in the result of the primary segmentation, take
W as a background block and set ()0MW
=
.
Fig.5(c) shows the secondary segmentation result of
image Fig.3(a) which has the remaining ridges not
removed by Neuro and by the primary segmentation.
More results are shown in Section 4.
4 EXPERIMENTAL RESULTS
The experiments use eight images, denoted as
image1~8 respectively: image1, image 2, and
image3 are shown in Fig.2(a), Fig.2(c) and Fig.3(a),
respectively, and images 4~8 are shown in Fig.6.
Fig.4 shows the segmentation results of the first 3
images by the proposed method. And their
segmentation results by the Neuro are shown in
Fig.2(b), Fig.2(d) and Fig.3(b), respectively. Fig.6
shows the segmentation results of the rest 5 images.
Segmentation of fingerprint image serves for
reducing the consumed time of image processing
and improving the accuracy of minutiae detection.
One of method to evaluate an automatic
segmentation method is to compare the segmented
image of the automatic algorithm with the manually
segmented image and then estimate the
segmentation accuracy of the automatic algorithm.
Also, we can use the accuracy of minutiae detection
for comparing two automatic segmentation
algorithms. The accuracy of minutiae detection can
be evaluated using EI (Error Index), EI=(a+b)/t
where a is the number of lost minutiae, b is the
number of spurious minutiae, and t the total number
of minutiae contained in the image. The value of t is
generally computed as the number of manually
labeled minutiae. The smaller the value of EI is, the
more accurate the algorithm is. We quantitatively
verify the proposed segmentation method only using
EI, since the accuracy of segmentation is obviously
shown in Fig.5 and Fig.6. The Error Indexes of
minutiae detection on each experimental image are
listed in Table 1. The average EI of the two
methods, Neuro and the proposed, on the
experimental images are respectively 1.27 and 0.49.
The proposed method produces spurious minutiae
much less than Neuro and greatly decreases the EI
value.
(
a
)
(
b
)
(
c
)
Figure 5: Examples of segmentation by the proposed
method. (a)result of image Fig.2(a), (b)result of image
Fig.2(c), (c)result of image Fig.3(a).
VISAPP 2006 - IMAGE ANALYSIS
350
FVC2002_DB3_48_8FVC2002_DB3_ 95_ 8 FVC2004_DB2_100_6FVC2002_DB3_ 97_ 2 FVC2004_DB2_28_2
Figure 6: Comparison of segmentation between Neuro and the proposed. Left column is the original images; Middle
column is the results by Neuro; Right column is the results by the proposed. The images in left column, from top to bottom,
are respectively denoted as image4, image5, image6, image7 and image8.
EXCLUDING THE REMAINING RIDGES OF FINGERPRINT IMAGE
351
Table 1: EI comparison.
Neuro The proposed Image Manually
labeled
True Spurious Lost EI True Spurious Lost EI
Image1 26 13 25 13 1.46 13 2 13 0.58
Image2 11 3 32 8 3.64 9 4 2 0.55
Image3 29 27 8 2 0.34 26 2 3 0.17
Image4 17 12 10 5 0.88 9 4 8 0.71
Image5 35 29 24 6 0.86 29 2 6 0.23
Image6 17 13 10 4 0.82 10 7 7 0.82
Image7 12 11 12 1 1.08 11 1 1 0.17
Image8 10 6 13 4 1.70 9 6 1 0.70
Average 20 14 17 5 1.27 15 4 5 0.49
4 CONCLUSION
Fingerprint segmentation is not a full-solved
problem in fingerprint recognition and mainly aims
to reduce time expenditure of image processing and
to improve minutiae detection. The main difficulties
of fingerprint segmentation are that low quality
regions are hard to be classified and that the
fingerprint images are often interfered with
remaining ridges which are the afterimage of the
previously scanned finger and are hard to be
removed especially when they appear clear
structures. It is difficult to correctly estimate the
orientations of low quality ridge regions, and a
manually recoverable region should be taken as
background if its orientations are falsely estimated.
Spurious minutiae are generally produced by
including manually or algorithmically unrecoverable
ridge regions as foreground. In order to accurately
remove unrecoverable regions and remaining ridges
and as a consequence to improve the minutiae
detection, this paper, following our previous work
(Zhu, 2005), proposed a method, primary
segmentation, to exclude non-ridge regions and
(manually or algorithmically) unrecoverable regions,
and then proposed the secondary segmentation to
reclassify the foreground blocks of the primary
segmentation to remove the remaining ridges. The
experiments show that the proposed method leads to
an improvement of minutiae detection.
ACKNOWLEDGEMENTS
This work was sponsored by the national natural
science foundation of China (Project No. 60373023)
and by science and technology research Foundation
of Hunan City College (No. 20057306)..
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