segmentation of low quality fingerprint images
TRANSCRIPT
Segmentation of Low Quality Fingerprint Images Diala Jomaa
Computer Science, DalarnaUniversity Planet Gatan1B 78453, Borlänge
ABSTRACT
A fingerprint image is a pattern which consists of two regions,
foreground and background. The foreground contains all
important information needed in the automatic fingerprint
recognition systems. However, the background is a noisy region
that contributes to the extraction of false minutiae in the system.
To avoid the extraction of false minutiae, there are many steps
which should be followed such as preprocessing and
enhancement. One of these steps is fingerprint segmentation. The
aim for fingerprint segmentation is to separate the foreground
from the background. Due to the nature and the poor quality of
fingerprint image, the segmentation becomes an important and
challenging task.
This paper presents a new algorithm to segment fingerprint
images. The algorithm uses four features, the global mean, the
local mean, variance and coherence of the image to achieve the
fingerprint segmentation. Based on these features, a rule based
system is built to segment the image.
The proposed algorithm is implemented in three stages; pre-
processing, segmentation, and post-processing. Gaussian filter and
histogram equalization are applied in the pre-processing stage.
Segmentation is applied using the local features. Finally, fill the
gaps algorithm and a modified version of Otsu thresholding are
invoked in the post-processing stage.
In order to evaluate the performance of this method, experiments
are performed on FVC2000 DB1. Segmentation of 100 images is
performed and compared with manual examinations of human
experts. It shows that the proposed algorithm achieves a correct
segmentation of 82% of images under test.
General Terms
Algorithms, Performance, Design, Reliability, Experimentation.
Keywords
Fingerprint Recognition System, Fingerprint, Otsu, Split and
Merge, Histogram Equalization, mean, variance and coherence.
1. INTRODUCTION Fingerprint is fully created at about seven months of fetus
development. It is unique and unchangeable during individual’s
life excluding the situation of accidents in the finger such as cuts
or injuries. A captured fingerprint image usually consists of two
components; the foreground and the background. The foreground
is the area of scanner surface which is in contact with a finger
surface. It includes the necessary information needed for
fingerprint recognition, while the background is the noisy area
which is located at the borders of the image.
Fingerprint segmentation is the process by which the foreground
is separated from the image background. The result of fingerprint
segmentation is a fingerprint image in which the background is
removed [2, 9].
Fingerprint segmentation is an important step in the automatic
fingerprint recognition systems because it improves the fingerprint
images so that features can be extracted from these images by the
automatic fingerprint recognition systems. Features such as
minutiae and singular points are especially important for the
reliable identification of fingerprints. Minutiae means small
details that can determine important local features in the
fingerprint image and singular points means regions that represent
distinctive shapes. When fingerprint images include a noisy
background, feature extraction algorithms extract a lot of false
features. Hence, developing good fingerprint segmentation
algorithm helps to discard the background, and thus reduces the
number of false features.
However, the presence of dust and grease in the scanner’s sensor,
the presence of some traces from previous image acquisition or
the dryness or the wetness of the finger can cause the
segmentation to be a challenging task.
Fingerprint segmentation can be achieved by two approaches:
block-wise based and pixel-wise based [12]. In the block-wise
approach, the fingerprint image is divided into blocks and each
block is classified into foreground or background based on
features calculated for the block. While in the pixel- wise method,
segmentation is achieved on the pixel level.
Based on the hypothesis that a small fingerprint fragment
resembles a two-dimensional sinusoidal function Marques and
Thome [10] used a neural network to detect the region of interest
in fingerprint images. Bazen and Gerez [3] invoked an optimal
linear classifier trained with three pixels features; mean, variance
and coherence to achieve segmentation. Weixin [13] presented an
improved active contour for extracting salient object contours in
fingerprint images. Helfroush and Mohammadpour [7] used a
combination of three variance mean and ridge orientation features
and also employs the median filter as a post processing step.
In this paper, a new algorithm for the segmentation of fingerprint
images is presented. Segmentation is achieved by using a number
of rules related to global mean, local mean, local variance and
coherence. It includes pre-processing and post-processing stages
to enhance segmentation.
The rest of the paper is organized as follows. Automatic
fingerprint recognition system is described in section 2.
Segmentation methods and enhancement techniques are given
respectively in section 3 and 4. The features for fingerprint
segmentation are shown in section 5. In section 6 the proposed
algorithm is illustrated. The experimental results based on the
proposed method are displayed in section 7 and in section 8, the
conclusion is presented.
2. Automatic Fingerprint Recognition System Fingerprint recognition system is the oldest recognition system.
In the early twentieth century, fingerprint recognition becomes
accepted as a personal identification system in forensic.
Afterwards, different fingerprint recognition techniques, like
latent fingerprint acquisition, fingerprint classification, and
fingerprint matching were developed. At present, automatic
fingerprint recognition is in progress day after day not just in
forensic applications, but even in civilian applications.
Fingerprint recognition systems consist of the following parts
(figure 1):
- Sensing or Image acquisition
- Pre-processing
- Feature or minutiae extraction
- Matching
Figure 1. Fingerprint Recognition System
2.1.1 Sensing or Image Acquisition The acquisition of a fingerprint images was accomplished by
using off-line sensing or live-scan. Off-line sensing is defined as
ink-technique. An individual place his finger in black ink then his
finger is pressed in a paper card. However, live-scan scanners
become presently more frequent, because of its simplicity in
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usage. There is no need for ink. The digital image is directly
acquired by pressing against the surface of the scanner.
2.1.2 Pre-processing To simplify the task of minutiae extraction and make it more easy
and reliable, some preprocessing techniques are applied to the raw
input image. Enhancement and segmentation of the fingerprint are
the most commonly methods performed in the preprocessing step.
The principal aim of enhancement is to improve the clarity of
ridge in the recoverable area in the image and to assign the
unrecoverable ridges as a noisy area. Recoverable region is
considered when ridges and valleys are corrupted by a small
amount of dirt, ceases, or other kind of noise. Unrecoverable
region are the regions which are impossible to recover them from
a very corrupted and noisy image [14].
However the primary purpose of segmentation is to avoid
extraction of feature in the background that is in reality considered
as a noisy area [8]. Segmentation indicates the separation of
fingerprint area or foreground from the image background. Due to
the streaked nature of the fingerprint area, a simple thresholding
technique is not sufficient. In addition to the presence of noise in a
fingerprint image, fingerprint segmentation requires more robust
and strong techniques [1].
2.1.3 Feature extraction After preprocessing step, the segmented and enhanced fingerprint
is further processed to identify the main and distinctive minutiae.
Most of the minutiae extraction methods necessitate the
fingerprint gray-scale image to be transformed into a binary
image. The acquired binary image is forwarded to a thinning stage
to reduce the thickness of the ridge to one pixel ridge. Afterwards,
the minutiae are simply detected by a simple image scan.
Due to the characteristic of the pixel that corresponds to minutiae,
the simple scan image is one of many methods developed for
minutiae detection. It depends on calculating crossing number of a
pixel. The crossing number is the half sum of the differences
between pairs of adjacent pixels in the 8-neighborhood of p. Since
the minutiae pixel can be bifurcation, crossover, termination, and
so on. Therefore, the crossing number for minutiae must be
different from 2 [9].
To avoid the problems related to fingerprint binarization and
thinning, many methods have been proposed. Direct gray-scale
minutiae extraction is one of these methods. The basic idea of this
algorithm is to track the ridge lines in the gray-scale image by
going according to the local orientation of the ridge. When a ridge
line terminates or intersects another line, the algorithm detects this
location as a minutiae point [9].
2.1.4 Matching Algorithms that extract important and efficient minutiae, will
improve the performance of the fingerprint matching techniques.
The features extracted of the input image are compared to one or
more template that was previously stored in the system database.
Therefore the system returns either a degree of similarity in case
of identification or a binary decision in case of verification.
Minutiae-based and correlation-based matching techniques are the
most common techniques in fingerprint matching. In Minutiae-
based techniques, first systems extract the minutiae in both images
then the decision is based on the correspondence of the two sets of
minutiae locations. However in correlation-based techniques
compare two fingerprints based on their gray level intensities.
First it selects relevant templates in the primary fingerprint then it
uses template matching to locate them in the secondary image and
compare positions of both fingerprints [4]. In correlation-based
techniques, the propagation of errors in minutiae extraction step is
avoided. It doesn’t require many preprocessing steps.
Nonetheless, minutiae-based technique is the most widely used
technique in fingerprint matching.
3. SEGMENTATION METHODS Generally, Image segmentation methods are classified into two
categories, discontinuity and similarity of intensity value. In the
discontinuity-based categories, segmentation can be defined as
edge-based segmentation that subdivides an image based on
abrupt changes in the intensity. In the similarity-based categories,
the segmentation is related to partitioning an image into regions
according to their similarity. The similarity is a measure that is
defined in advance depending on the fundamental problem in the
image. This measure can be a specific intensity level, mean value,
variance value, and so on. Point, line and edge detection are
examples of discontinuity methods. Also, threshold, Otsu,
splitting and merging and region growing are examples of
similarity-based methods. The combination between different
methods can give an improvement in segmentation performance.
3.1 Threshold method The main idea in threshold methods is to select a threshold T that
can separate objects from the background. This threshold can be
specified according to the intensity histogram. Histogram of an
image displays the gray-level values versus the number of pixels at
that value. Any pixel with gray level Tyxf ),( is assigned as a
foreground; otherwise the pixel is assigned as background see
formula 3.1.
255),( yxG If Tyxf ),(
0),( yxG If Tyxf ),(
(3.1)
For fingerprint images, the histogram shows the contrast of the
image and the distribution of the gray level. As shown below in
figure.2 the image is a bright image and no obvious gray level
point can be as thresholding point. Because of the nature of
fingerprint images, this algorithm cannot apply a simple
thresholding technique.
Figure 2. Histogram for bright fingerprint
3.2 Optimum Global Thresholding, Otsu’s
method The basic idea of Otsu’s method is that an optimum threshold
maximizes the separation between classes with respect to intensity
value. According to maximizing the between-class variance, this
method is optimum. Otsu’s algorithm can be presented as the
follows [12]:
- Compute the normalized histogram of the input image by
using equation (3.2)
MN
nP i
i
(3.2)
Where in
is the number of pixels with intensity i, MN is total
number of pixel
- Set through possible threshold as shown in equation (3.3) kkT )( (3.3)
Where L is the maximum intensity level in the image and is
between 0 and L-1
- Compute the cumulative sum by using equation (3.4) k
i
iPkP0
)( (3.4)
- Compute the cumulative means by using equation (3.5) k
i
iiPkm0
)()( (3.5)
- Compute the global intensity mean by applying equation
(3.6) 1
0
)(L
i
iG iPm (3.6)
- Compute the between class variance by using equation
(3.7)
))(1).((
)))(1).((( 22
kPkP
kPkPB
(3.7)
- Compute the Global variance by using equation (3.8) 1
0
2)(
L
i
iGG Pmi (3.8)
- Obtain the Otsu threshold as in equation (3.9)
)max(2
BK (3.9)
- Obtain the seperability measure by using equation (3.10)
2
2
G
B (3.10)
As explained before, the objective of this method is to find one
threshold to give the best separation between classes. Due to the
quality and nature of fingerprint, Otsu’s method cannot produce a
good separation between the foreground and the background. In
addition, the presence of dark contrast in the foreground, the
algorithm can destroy the details for ridges and valleys. Also the
formal Otsu’s Algorithm may not preserve the content of the
foreground (figure 3).
Figure 3. (a) Original image 1.4, (b) Segmented image by using
original Otsu
3.3 Split and Merge In this section, Region Splitting and Merging technique is
introduced here to segment the image by finding the regions
directly. The principal idea of this method is to iteratively split no
similar regions and merge similar regions. The approach for this
algorithm is to subdivide first the image into smaller and smaller
quadrant regions. This type of splitting is called quad trees, each
node has exactly four descends. The root for this tree corresponds
to the entire image and each node corresponds to the subdivision.
The region of interest has some clear characteristics that should be
taken as a predicate for this region segmentation. This predicate
can be considered as mean, variance or color. The splitting takes
place if the region doesn’t satisfy the predicate, otherwise these
splitting stops (figure 5). If only splitting used, the algorithm
contains only adjacent regions with same properties. Therefore,
the merging technique is needed to combine two adjacent regions
with identical properties.
Figure 5.Quad tree splitting
Split and merge algorithm is summarized by the following steps
[5]:
- Define a typical predicate to segment the image
- Split the image into four disjoint quadrants in the case of
the region doesn’t satisfy the predicate
- Merge two adjacent regions if they satisfy the predicate.
- Merge two regions in different level if they satisfy the
predicate
- Merge small region with the most similar adjacent region
- Stop merging and splitting until no regions remain
unchecked.
(a) (b)
Figure 6. (a) Original image 1.4, (b) Segmented image by using
Split and merge with variance 100.0 as a predicate
In the figure above (figure 6), it shows the result obtained by
applying Split and Merge algorithm in a fingerprint image. This
algorithm segment the image by using the variance as a predicate.
The algorithm starts with image. If the variance of the image is
greater than 100.0, the algorithm divides the image into smaller
quadrant regions. If the variance is greater than 100.0 for any
quadrant, the algorithm subdivides that quadrant into sub quadrant
and so on. After splitting the image, merging technique starts by
combining two regions if their variance is less than 100.0. As seen
in the resulting image, Split and Merge helps to nearly extract the
foreground from the background. Due to the predicate used in this
segmentation, the foreground has not effectively partitioned.
Variance is not a sufficient predicate to segment an image without
any problems. Looking for efficient feature or suitable predicate is
needed.
For that reason, the explanation of some important feature in
fingerprint segmentation is important to discuss.
4. Image Enhancement As described in the previous section, when the image is acquired,
the quality of fingerprint image can be affected by different
factors. However, fingerprint images with low contrast or false
traces ridges or noisy complex background cannot be segmented
correctly by segmentation methods. Therefore, it is required to
improve the quality of the image by applying some enhancement
techniques. Some of these techniques that were used in this thesis
are Gaussian Filter, Histogram Equalization and morphological
reconstruction.
4.1 Gaussian Filter Gaussian Noise appears in the image caused by factors such as
poor illumination and high temperature for sensor. Gaussian filter
is used to smooth the image and remove these noises. This filter is
similar to mean filter which is called averaging filter. The degree
of smoothing of Gaussian is expressed in term of which the
standard deviation of the distribution. In 2-D the Gaussian
distribution has the form of (4.1).
G(x,y) = 2
22
2
22
1yx
e
(4.1)
The goal of Gaussian Filter is to use this distribution as a point
spread function which can be performed by convolution mask.
The meaning of convolution is the process of moving the mask
from the upper-left corner to the lower-right corner and replacing
the value of the center pixel in the image by the value of g(x,y).
G(x,y) is calculated by the sum of products of the filter
coefficients and the corresponding image pixels in the area
spanned by the filter mask. Normally, the filter mask is a two
dimensional array in which the values of the mask coefficients
affect the nature of the image [5]. Therefore Gaussian filters
smoothes the image by using the following mask based on
discrete approximation to the Gaussian function (Figure 7).
273
1
Figure 7: Gaussian mask
Figure 8 shows the result of using Gaussian filter with = 5.
This filter blurs the fingerprint image by reducing some noises in
the background.
1 4 7 4 1
4 16 26 16 4
7 26 41 26 7
4 16 26 16 4
1 4 7 4 1
(a) (b)
Figure 8. (a) Original image 1.4, (b) Noise reduction with
Gaussian Filter
4.2 Histogram Equalization The usage of Gaussian Filter in fingerprint images help efficiently
to enhance the image in case of the presence of noise such that
image acquisition noises. However, this filter cannot enhance an
image which is affected by contrast problem. Therefore Histogram
Equalization is a good solution for this problem. Histogram
Equalization is the most common technique for improving the
appearance of a poor image. It is the technique to get the
histogram for the destination image as flat as possible. Histogram
Equalization defines a mapping of gray level p into gray level q
such that the distribution of gray level q is uniform. This mapping
stretches the contrast of gray level near the maxima in the
histogram [6]. The probability density function of a pixel intensity
level kr is yield by formula (4.2).
n
nrp k
kr )( (4.2)
Where kr is between 0 and 1, k=0, 1… 255, kn is the number of
pixels at intensity level kr and n is the total number of pixels.
The new intensity value ks for level k is derived by formula (4.3).
k
j
jr
k
j
j
k rpn
ns
00
)( (4.3)
By applying histogram equalization in a fingerprint image, the
contrast is increased in most of fingerprint pixel. The first image
in figure 9 shows the original image with its corresponding
histogram and the equalized image with its corresponding
histogram. As expected, the histogram of the original image (a) is
concentrated on the light side of the intensity scale. The result of
histogram equalization show important improvement in the
contrast. In the equalized histogram (d), the intensity values cover
the entire gray scale. Therefore, the significant contrast
differences between the original histogram and the equalized one,
illustrate the power of histogram equalization as a principal
contrast enhancement tool.
(a) (b)
(c) (d)
Figure 9. (a) Original image, (c) histogram of the original
image, (b) equalized image, (d) histogram of the equalized
image
5. FEATURES FOR FRINGERPRINT
SEGMENTATION Fingerprint features must reflect both the gray level of fingerprint
and the direction of ridge lines. However, the complicated
construction of fingerprint pattern and the imbalance in the
contrast, require local feature instead of the global feature. Local
Mean and variance are the features for gray-level based methods
while coherence is the feature for direction based method. The
combination for these features in one algorithm show efficiently
the distribution of the pixels for ridges and valleys in the image.
Coherence feature indicates the strength of the local window
gradients centered on the processed point along the same
dominant orientation.
Local mean, local variance and local coherence [7] are calculated
as the following formulas (5.1) and (5.2).
w
meanIw
Variance 2
2)(
1
w
Iw
mean2
1
(5.1)
Where I is the intensity and w is the window size centered on the
processed pixels.
)/()(4)( 22
yyxxxyyyxx gggggcoh
w
xxx Gg 2 ,
w
yyy Gg 2 , y
wxxy GGg
(5.2)
Where xG and
yG are corresponding horizontal and vertical
gradient components which are given by Sobel operators.
Background of a fingerprint image is the area where the finger
does not touch the sensor which means that background is the
white pixel in the image. Therefore, the local mean in the
background is higher than that of the foreground. In contrary to
mean, local variance is higher in the foreground parts compared
with background. This is applied to coherence which is high in the
foreground and low in the background. The spatial distribution of
these three features is shown in figures 10, 11 and 12.
Figure 11. Distribution of mean in image 1_4.
Figure 12. Distribution of variance in image 1_4.
Figure13. Distribution of coherence in image 1_4.
Another interesting relationship among mean, variance and
coherence is shown in figure 14. This 3D distribution of the local
mean, variance and coherence of sub-images extracted from
image 1_4 of FVC2000 DB1 shows clearly that a fingerprint
image has two distinctive spaces of foreground and background.
Figure 14. Relationship among mean, variance and coherence
of image 1_4.
6. THE PROPOSED METHOD The detailed steps of the fingerprint segmentation algorithm are
depicted in figure 15. In this paper, fingerprint segmentation is
achieved by three stages:
Figure 15. The proposed algorithm.
6.1 Pre-processing Fingerprint images with low contrast, false traces ridges or noisy
complex background cannot be segmented correctly. Therefore, it
is required to enhance the image. Techniques used in this paper
are Gaussian Filter, and Histogram Equalization. Gaussian Filter
is used to smooth the image and hence background areas. This
step together with split and merge technique, which is applied in
the next stage, will collect pixels with similar gray levels into big
areas. Histogram Equalization is invoked in this stage too. When
the global mean of the image under consideration is higher than a
certain threshold, which mean a bright image, histogram
equalization is used to enhance the image by reducing the
brightness of the image.
6.2 Segmentation In this stage, split and merge technique is applied to collect
similar background areas after the smoothing process. In order to
separate the foreground from the background, the image is divided
into a number of non-overlapping sub-images of size 10x10 pixels
and the local mean, local variance, and local coherence are
computed for each sub-image.
The global mean together with the three aforementioned
parameters are used to build a rule based system to segment the
foreground and the background of the image under consideration.
The result of this rule based system is to decide whether a certain
block is a foreground or a background. When a certain block is
decided to be a background, a set of white pixels are placed in this
block, otherwise the image pixels are kept.
The segmentation is achieved by dividing the active space shown
in figure 14, which is specified by minimum and maximum values
of local mean, variance and coherence, into a number of
subspaces. Then the global mean is invoked to build a simple
hierarchy of if then rules to decide whether a certain block
belongs to a background or a foreground region.
6.3 Post-processing The segmented fingerprint image may contain isolated
background blocks which are surrounded by foreground blocks.
Obviously, these background blocks are foreground in the original
image. A simple post-processing technique is proposed to
eliminate the presence of these isolated blocks. It is as follows, see
figure 16:
For all blocks in the image, if a background block (i,j) is found
o Check the N4 neighbor blocks which are located at (i, j-1),
(i, j+1), (i-1, j) and (i+1, j) for the presence of foreground.
o If two or more of these neighbors are foregrounds,
change all of the pixel in the block (i,j) back to their
original value before any segmentation.
After filling the foreground with the missing blocks, a modified
version of Otsu thresholding [11] method is applied. The modified
algorithm works locally in the blocks of the image to find the
corresponding optimal threshold for each block and segments the
image into white background and black foreground.
Figure 16. Filling gaps in a block image
6.4 Modified Otsu’ method Noise and non uniform illumination in the foreground and
background play a principal role in the performance of original
Otsu’s algorithm. Also, the algorithm goes through the entire
image to find an optimal threshold to further use in segmentation.
In this paper, a new version of Otsu method is developed. The
developed algorithm seeks locally in small regions of the image
and find corresponding optimal threshold for every region.
The main idea of this method is to extract the foreground from the
background by applying the global Otsu’s method and to preserve
ridges lines in the foreground by using Otsu’s in image
partitioning (figure 4).
(a) (b) (c)
Figure 4. (a) Original image 1.4, (b) Image segmented into 900
regions using Otsu thresholds in each region, (c) Segmentation
of (a) using modified Otsu
7. EXPERIMENTAL RESULTS The proposed algorithm is tested on 100 fingerprint images which
are selected randomly and without repetition from FVC2000
database DB1. These images are collected by using two small-size
and low-cost optical sensors.
To evaluate the efficiency of this algorithm, human expert
examines the results of the segmentation algorithm from these
random images. Figure 17 shows a number of successful
segmentation achieved by this algorithm.
Figure 17. Correctly segmented images from FVC2000
fingerprint database.
In order to evaluate this method quantitatively, a four-level
segmentation scheme is suggested. The number of correctly
segmented blocks in the image is measure on which this scheme is
based. This scheme is defined as follows:
Good, when more than 90% of the background blocks are
segmented correctly.
Almost Good, when 75% - 89% of the background blocks are
segmented correctly.
Almost Bad, when 60% - 74% of the background blocks are
segmented correctly.
Bad, when less than 60% of the background blocks are
segmented correctly.
The results of classification into four categories are shown in
figure 18.
Good
Almost good
Almost bad
Bad
Figure 18. Four samples from FVC2000 which show the good,
almost good, almost bad and bad categories.
This scheme is applied on the tested images extracted from
FVC2000 DB-1 and the results of segmentation are depicted
Table 1. According to this scheme, Good and Almost Good results
can give 82% of the images under test.
Table 1. Results of different categories
Result Percentage
Good 66%
Almost Good 16%
Almost Bad 4%
Bad 14%
8. CONCLUSIONS This paper presents a new algorithm for the segmentation of
fingerprint images. The proposed algorithm is implemented in
three stages; pre-processing, segmentation, and post-processing.
Segmentation is achieved by using a number of rules related to
global mean, local mean, local variance and coherence.
Experiments of testing this algorithm show that it is able to
segment 82% of images used for testing. Further analysis to these
results can reveal to a set of modifications of this algorithm to be
implemented in the future. These modifications can be either the
use of Support Vector Machine (SVM) classifier or Fuzzification
techniques to replace the rule based system, which can lead to
better segmentation results. The scheme was applied on the tested
images extracted from FVC2000 DB-1. More intensive tests can
be done in other FVC2000 databases.
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