Personal Authentication Using Finger Vein Pattern
and Finger-Dorsa Texture Fusion
Wenming Yang
Xiang Yu
Qingmin Liao
1
Visual Information Processing Lab., Graduate School at Shenzhen, Tsinghua University
2
Tsinghua-PolyU Biometric Joint Lab.
Graduate School at Shenzhen, Tsinghua University, the University Town, Shenzhen, 518055, China
0086(0)75526036461
[email protected]
[email protected]
[email protected]
vein[4], and fusion of face and palmprint[5]. Among them,
techniques based on vein fusion have increased due to their
unique stability and anti-counterfeiting capability. In comparison
with hand, palmprint and face, features based on finger are
smaller in, and there are more alternatives. Certainly, finger
authentication system is with lower device requirement, smaller
device volume and more flexibility in dealing with different
applications. Therefore, in this paper, we focus on extraction and
fusion of finger vein and dorsal texture for personal authentication.
ABSTRACT
Personal authentication has attracted great attention due to its
large potential of security application, and many researches have
shown that fusion of features or decisions obtained from various
single-modal biometrics verification systems can enhance the
overall performance of system. In this paper, we proposed a novel
multimodal biometric approach fusing finger vein pattern with
finger-dorsa texture. Firstly, Finger Vein image and finger-dorsa
image from the same finger are captured simultaneously, and a
method is designed to segment Regions Of Interest(ROI) of vein
image and dorsal image. Secondly, two strategies are designed to
extract finger vein pattern and finger-dorsa texture respectively.
Vein extraction strategy consists of four steps: local thresholding,
modified line tracking, thorough probability map creating and
directional neighbor analysis. Gray normalization is performed on
finger-dorsa image to extract main finger-dorsa texture. Thirdly,
the binarized vein pattern and normalized dorsal texture are fused
into one feature image. Finally, a block-based texture feature is
proposed for personal authentication. Experimental results
showed that the proposed fusion method outperforms any one of
finger-dorsa and finger vein methods.
Recently, finger vein identification technique has become the
most favorite and novelest biometric method due to its low device
requirement and high anti-counterfeiting [6, 7, 8, 9]. Actually,
Miura [6] has made original finger vein pattern extraction. His
algorithm mainly uses the finger vein’s valley character in pixel
gray and dose repeated line tracking to figure out the vein lines.
However, the extracted vein may be uncontinuous. Yu [7] made
progress in modifying the traditional templates into directional
templates. Then, according to three separate threshold steps, the
final result is given. This algorithm can overcome time consuming
problem, but the threshold parameters are set by hand. And it
lacks the flexibility in dealing with different light environment as
the threshold is not adaptive. The traditional method [8], which is
based on local thresholding, can give the most part of finger vein
but the pattern is always intermittent. Considering all those
methods, we propose a novel finger vein pattern extraction
strategy which could mainly overcome the disadvantages. Our
vein extraction strategy consists of local thresholding, modified
line tracking, thorough probability map creating and directional
neighborhood analysis.
Categories and Subject Descriptors
I.5.4 [Pattern Recognition]: Application – Computer vision and
Signal processing
General Terms
Algorithms, Design, Security, Verification.
This paper is organized as follows: Section 2 describes our finger
vein and finger-dorsa imaging and ROI segmenting methods
based on the utilization of near-infrared light and common gray
camera. Section 3 and 4 elaborate vein pattern and dorsal texture
extraction strategies respectively, both theoretically and
experimentally. Fusion and recognition approaches are introduced
in Section 5. Experiments are performed on a small sample
database, and the results are shown in section 6. Section 7
concludes this paper with a discussion.
Keywords
Multimodal biometric technology, feature fusion, finger vein
pattern, finger-dorsa texture.
1. INTRODUCTION
With greater flexibility in feature selection and higher security
requirements, multimodal biometric techniques have attracted
great attention, such as fusion of hand shape and texture[1],
fusion of palm prints and hand shape[2,3], fusion of palmprint and
2. IMAGING AND ROI ACQUISITION
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Figure 1.illustrates the image acquisition method. The upside gray
camera gets finger-dorsa image while the downside one for finger
vein image. Two arrays of near-infrared(NIR) led (wavelength
890 nm) are fixed on top-left and top-right to the finger. Infrared
light penetrates the finger and then pass through the infrared light
filter whose cutoff wavelength is 1000 nm. After upside gray
905
camera and image card, the final images are sent to computer.
Since the hemoglobin in blood of vein would absorb more NIR
light than the surrounding environment, vein region in the image
appears darker than other areas. Figure.2 shows raw images.
Figure 3. Valley assumption Figure 4. Line tracking process
The middle gray of cross section is usually much lower than both
sides. If at one point this characteristic is found, it is assumed that
the point is within vein region. Considering there may be fuzzy
region or noise, we also make a probability map to discriminate
different regions.
Figure 1. Image acquisition
⎧1 − e I (b )− I ( a ) , if I (b ) < I ( a )
⎪
P r g r a y ( a , b ) = ⎨ 0 .1,
e ls e if I (b ) = I ( a )
⎪ 0,
e ls e
⎩
Figure 2. Raw finger image
After obtaining raw images, ROIs need to be segmented from raw
images. In that finger edge is clear in vein image, and a
horizontal Sobel edge detection is performed in raw image. Thus
ROI for vein image is obtained. Since there is invariable position
relationship between vein image and dorsal image, ROI for dorsal
image can be segmented according to ROI for vein image directly.
M
(3)
( x , y ) = P r g ra y ( s , p ) * P r g ra y ( t , p )
where M
P2
( x , y ) denotes the probability and Prgray ( a , b )
represents the dissimilarity of different gray levels. Then line
tracking algorithm is modified in reference to Figure 3 and Figure
4.
3. VEIN EXTRACTION STRATEGY
3.1 Local Thresholding
Step 1: for current pixel, if it is not visited, do local
thresholding to get M P ( x , y )
In our system, near-infrared finger vein image is always of low
quality because the capturing device is common and it is sensitive
to noise. Thus, conventional image processing methods such as
global threshold, histogram equalization are invalid. As the points
in finger vein region are always darker than non finger vein
region, we can focus on each point to figure out whether it is
darker than its neighborhood.
1
Step 2: find the valley characteristic and get M
P2
(x, y)
Here the direction of cross section should be determined
exactly.
Step 2.1: search for eight-neighboring directions
Step 2.2: calculate coordinates of point s and point t
W
W
(5)
s x = xc −
s in θ (4) s y = y c +
cos θ
2
2
The lower the gray value of a pixel is, the more possible it is in
vein region. Thus, we generate a probability map M P ( x , y )
1
⎧⎪ 1 − e ( I ( x , y ) − I ( x , y ) ) , if I ( x , y ) < I ( x , y )
M P1 ( x , y ) = ⎨
else
⎪⎩ 0,
P2
(2)
t x = xc +
(1)
where
W
sin θ
2
xc and yc are
(6)
t y = yc −
W
cos θ
2
(7)
the horizontal and vertical coordinates of
where I ( x , y ) is pixel gray, I ( x, y ) is mean of I ( x , y ) in
20*20 pixels neighborhood.
current pixel p while θ is direction and W is valley width.
Step 2.3: calculate M P ( x , y ) between s, t and current pixel.
3.2 Modified Line Tracking
( x , y ) of eight directions as the
final M P ( x , y ) . Then the corresponding direction is the
2
tracking direction.
Step 3: create probability to get M p ( x , y ) . The operation is
illustrated in the next section. If M p ( x , y ) is larger than a
2
Step 2.4: find the largest M
Let M P ( x , y ) denote the probability map for part of vein
pattern after the local thresholding operation. Still there are some
regions which are indeed the vein but not found. Another
characteristic of finger vein is the valley cross-sectional
brightness profile[6].
1
P2
given threshold (experimentally 0.5), calculate the next tracking
point, mark the current point as visited and go to step 1; else
break out the line tracking loop.
3.3 Thorough Probability Map Creating
M P1 ( x , y ) and M P2 ( x , y ) can be obtained by processing of
section 3.1 and section 3.2. These two probability maps both
reflect finger vein pattern. One is statistical description, and the
906
Step 6: compare ϕ with θ . According to the difference
between these two angles, make decision whether
other is geometrical one. In order to achieve more convincing
vein region, data synthesis is done.
⎧⎪max(Mp1 (x, y), Mp2 (x, y)), if max(Mp1 (x, y), Mp2 (x, y)) > 0.8 (8)
Mp (x, y) = ⎨
Mp2 (x, y),
else
⎪⎩Mp1 (x, y)
( xc , yc ) should be added to Gh
⎧ 1, i f | ϕ − θ |< Τ θ
G add = ⎨
e lse
⎩0,
Although threshold 0.8 is an experimental parameter, as it is a
reference of probability which varies from 0 to 1, it can remain
invariant to different vein image as long as the feature of vein is
not changed. If the larger of the two is less than the threshold, it is
more likely that current point is out of vein region. we update
M p ( x , y ) by multiplying two probability values.
Τθ
Step 8: if step 2 runs over, get the final binary map.
G fin a l = G h + G a d d
In this stage, ROI for finger-dorsa image is normalized in gray
according to the following equation:
I n ( x , y ) = dm + ds * s (
and Gl . The margin of these two maps contains
both noise and information.
(8)
According to the strategies above, finger vein pattern image
and finger-dorsa texture image
⎧ I ( x, y )
F ( x, y ) = ⎨ n
0
⎩
region.
Step 4: find the direction between current point and its nearest
Gh
( xn , yn )
P2
ifG
final
( x , y ) = 255
else
(15)
(10)
− yc
y − yc
< 0
) + π , xn ≠ xc & n
− xc
xn − xc
xn = xc
( xc , yc ) is current point.
section direction of ( xn , yn ) .
(a)
(b)
(c)
(d)
(e)
Figure 5 (a)ROI for vein; (b)vein pattern; (c)ROI for doral
image; (d)finger-dorsa texture; (e) feature image
is neighbor point and
Step 5: find the cross
here we
each M
yn − yc
≥ 0
xn − xc
attained, and their sizes
Figure 5 illustrates the results for several steps above. Obviously,
feature image obtained from fusion contains both topology
structure of finger vein pattern and certain finger-dorsa texture
information.
in Gh , in which it is assumed that all the points belong to vein
xn ≠ xc &
I n are
Gfinal
have been normalized to 128*256 pixels. To perform personal
authentication, we fuse the two images into one feature image F:
Step 3: for every new point in ΔG ' , find its nearest neighbor
− yc
),
− xc
(13)
5. FUSION AND FEATURE VECTOR
COMPUTING
The margin changes from null to ΔG , when this threshold
declines from h to l. In this process, there might be a most suitable
threshold. As finger vein’s major axis is almost along with the
finger, we made criteria to judge in ΔG which belongs to the
vein region. The detail is described as below.
Step 1: assign threshold h and l, and get margin ΔG
Step 2: let parameter t vary from h to l,
(9)
ΔG ' = Gt − Gh
yn
⎧
⎪ a rc ta n ( x
n
⎪
⎪
yn
ϕ = ⎨ a rc ta n (
xn
⎪
⎪π
,
⎪
⎩ 2
ROI ( x , y ) − lm
)
ls
where dm is the desired mean, ds is the desired standard variance,
ls and lm are the local mean and the local standard variance in 5*5
pixels block, s( ) is nonlinear sigmoid function as follows:
e x p (α t ) − 1
(14)
s (t ) =
,α > 0.
e x p (α t ) + 1
Thus finger-dorsa texture is also enhanced.
To overcome those shortcomings, we set two thresholds. One
could be high, and allow some loss of data; the other is low and
guarantee that data is redundant. In this way, two binary maps are
neighbor in
(12)
4. DORSAL TEXTURE SEGMENTATION
The thorough probability map almost contains whole information
of vein pattern. However, if we simply set a threshold and get the
binary map, the result may lack enough information or include
useless information. Too high threshold cuts off much positive
points while too low threshold brings in negative points.
ΔG = Gl − Gh
is an experimental variable which should be adjusted
Step 7: if the points of current threshold t are all visited, go to
step 2.
3.4 Directional Neighborhood Analysis
Gh
(11)
according to the noise.
M p ( x , y ) can be simply translated into a gray map by
multiplying the probability by 255.
generated as
or not.
search
(x, y)
Again
in its eight neighbors and calculate
as we did in section 3 step 2.4, the
corresponding direction is
θ
when M
P2
To achieve effective feature vector, we partition feature image F
into grids with the same size, and compute the gray sum of all
pixels of every grid. All gray sums are aligned into one feature
vector in fixed order. Figure 6 illustrates one gridding sample. For
feature image with size of 128*256, If every grid is with 16*16
( x , y ) reaches the
maximum.
907
pixels, the feature vector is 128 dimensional. Based on the feature
vector, we utilize Euclid distance between registered sample and
sample to be tested as decision, namely, if the distance is larger
than a preset threshold, the two samples don’t match; otherwise,
they are from the same person.
in our experiments. Fusion approach for vein pattern and dorsal
texture is simple but promising and applicable. Based on fusion,
we extract feature vector by gridding fusion image, and perform
identification. Testing result demonstrates the proposed
multimodal method is more robust than single feature method,
and finger vein method is more reliable than finger-dorsa texture
method. In future work, we will collect a larger scale sample
database to further evaluate the performance of the proposed
multimodal system and explore better person authentication
approaches.
8. ACKNOWLEDGMENTS
The authors would like to thank the three anonymous referees
whose comments and suggestions have greatly improved this
paper.
Figure 6. Gridding sample
6. EXPERIMENTS
To verify finger vein extraction strategy, we compare our results
with two existing methods, Miura’s method[6] and local
thresholding method[8]. The experimental results are shown in
Figure 7.
9. REFERENCES
[1] Kumar A., Zhang D., Personal recognition using hand shape
and texture, IEEE transactions on Image Processing, 2006,
15(8), pp.2454-2461
[2] Michael G., Tee C., Andrew G. et al., A single-sensor hand
geometry and palmprint verification system, Proceedings of
the 2003 ACM SIGMM workshop on Biometrics methods
and applications, 2003, pp.100-106
[3] Kumar A., Zhang D., “Combining fingerprint, palmprint and
hand-shape for user authentication”, The 18th International
Conference on Pattern Recognition (ICPR'06), 2006, 4,
pp.549-552
(a)
(b)
(c)
(d)
Figure 7. (a) source image; (b) Miura’s method; (c) local
thresholding method; (d) our method
We establish a small image sample database. A total of 120 set of
samples have been collected from 60 users, whose index fingers
from both left and right hands are captured via our imaging
system. Each user has two set of samples, each of which include
of four vein images and four dorsal images. In our identification
recognition experiments, two images were selected randomly
from the same subject for registration and another two images
were used as testing data. We use vein pattern and finger-dorsa
texture before fusion as final feature respectively to verify our
multimodal method, also Miura’s recognition method[6]. The
comparison result is given in Table 1.
Finger vein
Finger-Dorsa
Fused feature
97.5%
98.3%
92.7%
100%
J.-G. Wang, W.-Y. Yau, A. Suwandy and E. Sung, “Person
Recognition by fusing palmprint and palm vein images based
on “Laplacianpalm” representation”, pp.1514-1527, 2008.
[5]
X. Jing, Y. Yao, D. Zhang, J. Yang and M. Li, , “Face and
palmprint pixel level fusion and Kernel DCV-RBF classifier
for small sample biometric recognition,” Pattern Recognition
2007, 40, 11, pp.3209-3224.
[6] N. Miura, A. Nagasaka and T. Miyatake, “Feature extraction
of finger-vein patterns based on repeated line tracking and its
application to personal identification”, Machine Vision and
Applications, 2004, 15, pp.194-203
[7] Yu Chengbo Qing Huafeng and Zhang Lian, “A Research on
Extracting Low Quality Human Finger Vein Pattern
Characteristics”, The 2nd International Conference on
Bioinformatics and Biomedical Engineering, (ICBBE 2008).
16-18, May, 2008, pp. 1876-1879.
Table 1. Identification recognition rate comparison
Miura’s
[4]
[8] Kejun Wang, Qinghang Guo, Dayan Zhuang, Zhanying Li
and Hongxia Chu, “The Study of Hand Vein Image
Processing Method”, Proceedings of the 6th World Congress
on Intelligent Control and Automation, 2006.
7. CONCLUSION AND DISCUSSION
In this paper, a prototype of multimodal biometric system using
finger vein pattern and finger-dorsa texture fusion has been
developed. A simultaneous finger vein and finger-dorsa imaging
system is designed. According to the fixed position relationship
between vein image and dorsal image, the two corresponding ROI
for vein and dorsal can be segmented readily. An effective finger
vein pattern extraction strategy is proposed, which is also proved
[9] Mulyono, D. and H. S. Jinn, “A study of finger vein
biometric for personal identification”, International
Symposium on Biometrics and Security Technologies
(ISBAST2008),23-24 April, 2008, pp.1-8.
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