Engineering, 2013, 5, 534-539 Published Online October 2013 (
Copyright © 2013 SciRes. ENG
A Study of F eature S tab i l ity of Con tact-Less Imaging
Based on Palm Vein
Weiqi Yuan1, Wei Wu1,2, Lantao Jing1, Deqi Kong1, Lili Wang1
1Computer Vision Group, Shenyang University of Technology, Shenyang, China
2Information Engineering, Shenyang University, Shenyang, China
Received 2013
Palm vein hidden under the skin and its distribution feature is hard to be stolen, which makes the palm vein recognition
to be a high security biometric authentication method. Contact-less palm vein imaging can avoid the spread of disease,
thus expanding the application range of palm vein biometric authentication devices. However, due to the different un-
derstanding of the right imaging position and the change of fingers open degree, contact-less palm vein image acquisi-
tion led to a certain degree of translation, rotation , scaling and shear, tha t is, the image deformation. Image deformation
causes the imaging feature unstable. In this paper, the effect of image deformation to the stability of palm vein features
is studied by some similarity parameters. First, feature points in the palm were marked, con tact-less imaging and con-
tact imaging of palm vein were acquired. Then, this paper calculated the similarity parameters of the contact-less imag-
ing to contact imaging and gave corresponding analysis. Experimental results show th at contact-less palm vein imaging
was stable, and derived the linear regression equation of r elationship between sample space and the recognition rate: y =
0.000903x + 1.0332, coefficient of determination R² = 0.9824. This research provided effective and detailed data to
the study of conta ct-less palm vein recognition and gave powerful support to contact-less multi-feature fusion recogni-
tion based on hand.
Keywords: Palm Vein; Contact-Less Imaging; Stability
1. Introduction
Palm vein is a permanence and uniqueness physiological
feature of human [1,2]. Palm vein recognition as a new
family of biometric technology has gained more and
more attentions these years and it is expected to have a
wide range of security application. From the health pers-
pective, contact palm vein imaging may cause the spread
of germs, resulting in resistance to some users. This
problem is particularly exacerbated during the outbreak
of epidemics or pandemics like SARS and Influenza A
(H1N1) which can be spread by touching germs leftover
on surfaces. In the outdoor environment, this contact
palm vein device easily contaminated, affected its use in
the access control system. Contact imaging brings in-
convenience in actual use, which makes the developing
contact-less palm vein recognition instruments become
new direction. The “contact-less” in this research means
no guidance pegs were used to constraint the position of
the hand. The user’s hand can face the sensor naturally as
prompt during image acquisition. Obviously this con-
tact-less imaging can avoid the spread of germs and im-
proves ease of use.
Like the other biometric technology, the contact-less
palm vein recognition in this paper includes two stages:
registration stage and recognition stage. In registration
stage, there are three steps: preprocessing the captured
image firstly, then feature extraction and saving the fea-
ture to database for further matching lastly. In recogni-
tion stage, the captured image also taken the stage of
preprocessing, feature extraction then matches the feature
of the palm vein image in the database. Whether the two
images belong to the same hand was judged by the simi-
larity of the feature extracting from two images. Feature
extracting is the most important. At present, palm vein
feature extraction method can be roughly divided into the
following two categories: structure-based feature extrac-
tion, such as point features, line features; spatial fre-
quency domain transform based feature extraction, such
as the wavelet transform. Point feature extraction is a
simple way of extracting crossover point, bifurcation
point or endpoint of blood vessels in the palm image (as
shown in Figure 1). Literature [3-5] adopt this feature
extraction method. The deformation caused by con-
tact-less imaging will be reflected in the change of spatial
location of these feature points. This paper takes the
crossover point, bifurcation point or endpoint of blood
Copyright © 2013 SciRes. ENG
(a) (b)
Figure 1. NIR image of palm vein and feature point mark.
(a) NIR image of palm vein; (b) bifurcation point and
ending point m ark.
vessels in the palm image as the feature points. The paper
represents palm vein image deformation with the change
of these p oi nts in the spa c e posit ion.
To achieve the contact-less palm vein recognition, the
feature stability of palm vein is the question must be
discussed firstly.
Some other literature inferred “contact-less imaging”
also. Reference [6] captured the finger vein contact-less
with transmission illumination method, while our capture
method is reflection illumination method. Reference [7]
just mentioned “contact-less image”, but not clearly ex-
pressed how to realize. The contact-less imaging realized
in [8] used capture window, so it was not really contact-
less method. Reference [9] realized contact-less palm
vein recognition with a robust processing method, how-
ever, it didn’t involve the feature stability of imaging. To
the best of our knowledge, there is scant research which
focuses on the study of feature stability of contact-less
image bas ed on palm vein.
For fixed capture distance and illumination angle, co n-
tact imaging can guarantee for feature stable of palm vein
image. If the contact-less imaging is more similar to con-
tact image, the contact-less image is more stable. At the
same time, if the inter-class distinction between images is
bigger, the recognition rate is higher. To investigate the
feature stability of palm vein image, this paper designs
two experiments to ev aluate the similarity parameter and
recognition rate.
2. The Evaluation Methodology of Feature
Stability of Contact Image Based on Palm
We assume that the hands just take rigidity shape change
during the acquisition process.
If extracted all feature points in palm vein image by
programming, the result would be affected a lot by the
algorithm of preprocessing, feature extraction and feature
matching. To eliminate this eff ect, our research designed
an experiment to measure the similarity of contact-less
image to contact image. Additionally, palm vein image
quality is not good enough for every person. Extracting
feature points in this picture are difficult and are not ac-
curate enough. Actually, we want to get the offset of the
contact-less imaging to contact imaging, rather than the
actual location of feature points. Therefore, in order to
remove these impacts, this paper presents a simulation of
the palm vein feature point method, and accordingly de-
signed two experiments.
2.1. Evaluation Methods Based on Similarity
Contact-less imaging not only might lead to translation
and rotation, but also scaling and shear. The scaling is
caused by the different distance to sensor and the shear is
caused by the angle to sensor. The shear include hori-
zontal shear and vertical shear [10], shown in Figure 2.
Contact imaging might lead to translation and rotation.
Translation and rotation can be adjusted by the algo-
The effect on images led to the change of position of
the point in palm vein. The similarity of two images can
be measured by the change of position of feature points
in two images. Euclidean distance is one of the most
simplest and effective algorithms in measurement of the
similarity of two images. This research measured the
change of position of feature points in contact-less image
to position of feature points in contact image by Eucli-
dean distance. The similarity of contact-less image to
contact image was represented by this change.
We figured 9 feature points in the hands of volunteers
to represent the feature of the image of palm vein. The
position of feature point has no feature of vein; it only
represents the position of the point that meets some fea-
ture condition. In this experiment, the feature points were
just the constant points of palm vein. They represented
the bifurcation point and ending point on the palm vein.
The stability of these 9 feature points represented the
stability of the whole image. The change of these 9 fea-
ture points in contact-less image to contact image re-
presented the similarity of the two images. The 9 points
were specified as shown in Figure 2.
First, w e drew vertical line from the heel of thumb and
drew midline from middle finger, these two lines jointed
in the point marked point 5. Then, we drew a square of 4
cm length of sides with the central point was point 5 and
the coordinate was the heel of thumb and midline from
middle finger. Last, we marked the peak of square with
(a) (b) (c)
Figure 2. Level imaging and shear imaging. (a) Level imag-
ing; (b) Vertical shear imaging; (c) Level shear imaging.
Copyright © 2013 SciRes. ENG
point 1, point 3, point 9, point 7 and marked the middle
point of every length with point 2, point 6, point 8, point
4. We registered contact-less image to contact image
with point 5. After wiped off the deviation of rotary and
translation from contact-less image to contact image, the
other 8 points could represent the change from every
Experiment method: Every volunteer took one contact
image of palm vein, thr ee contactless images of palm
vein. After wiped off the deviation of rotary and transla-
tion from contact-less image to contact image, we called
it registration image. We compu ted Euclidean distance of
9 points in registration image to 9 points in contact image.
This Euclidean distance could represent the similarity of
contact-less image to contact image.
As shown in Figure 3, the coordinates of 9 character
points in contact image were dot1(x1,y1), dot2(x2,y2),
dot3(x3,y3), dot4(x4,y4), dot5(x5,y5), dot6(x6,y6), dot7(x7,y7),
dot8(x8,y8), dot9(x9,y9). The coordinates of 9 character
points in registration image were dot1’ (x1’,y1’), dot2
(x2’,y2’), dot3’ (x3’,y3’), dot4’(x4’,y4’), dot5’(x5’,y5’),
dot6’(x6’,y6’), dot7’(x7’,y7’), dot8’(x8’,y8’), dot9’(x9’,y9’).
We marked Euclidean distance of 9 points in registra-
tion image to the 9 points in contact image with sum. The
equation of sum was showed as (1).
9'2 '2
(()() )
ii ii
sumx xy y
=− +−
The similarity of contact-less image to contact image
was measured by the average value o f Euclidean distance
of 3 registration images to contact image.
According to Equation (2), we calculate the mean val-
ue of 3 Euclidean distances of registration imaging to
contact imaging. This mean value measured the similari-
ty of contact-less imaging and contact imaging by this
experimenter. Then we calculate the changes of distance
of point 5 to point 1, 2, 3, 4, 6, 7, 8, 9 separately. That is
the offset of 8 dire ction s as Figure 4.
sum n
2.2. Evaluation Methods Based on Recognition
This paper used recognition rate as another index to
measure contact-less imaging characteristics stability.
More significant the difference between the classes, (the
higher the recognition rate), and the more suitable for
subsequent identification .
In this experiment, we also draw 9 points on the palm
of experimenter to simulate the palm veins feature points.
It had difference with the first experiment. The length
and width of rectangle enclosed by point 1, point 2, point
Figure 3. 9 characteristic points on palm vein in experiment
Figure 4. Schematic diagram of 8chain code.
3, point 6, point 7, point 8, point 9, and point 4 were not
regular. T he second difference was the location of point
5 only enclosed in the rectangle, rather than the center,
the locat i on of the nine point s shown in Figure 5.
Every experiment took a contact imaging as the regis-
tration, extracted the coordinates of night points of this
image and stored them in the sample database. We cal-
culated the degree of angle α, shown in Figure 6.
The equation was following.
arccos 2
The degree of the angle α is also stored in the sample
database. The degree of the angle α is also stored in the
sample database. We took contact-less image of every
experiment as the login image. Extract the degree of an-
gle α, and nine coordinates of feature points as the match
The first step was to determine with angle α, chose
images with angle of α ± 1˚ into the next match, select
Point 4
Point 7
Point 8
Point 9
Point 6Point 6
Point 2
Point 2
Point 5
Copyright © 2013 SciRes. ENG
Figure 5. 9 characteristic points on palm vein in experiment
Figure 6. Schematic diagram of angle α.
minimum Euclidean distance of 9 feature points to de-
termine the same person, other wise identified as d ifferent
people. Recognition rate (Recognition Rate, RR) is cal-
culated as (4) [11]:
1 100%
NAA and NIA were the attempt times of the legiti mate
user and impostor in (4). NFR and NFA are times of the
false rejection and false acceptance. Programmatically
accomplish the above calculation.
This paper calculated the number of samples with 30,
60, 90, 120, 150, 180. With this change of samples the
regular pa ttern c ould be caug h t.
3. Experiment and Result Analysis
3.1. Similarity Experiment
To ensure the reliability of experimental data, ther e were
180 volunteers participating in the experiment, including
90 women. 180 volunteers aged 20 to 40 years old. The
experimenter divided volunteers into six groups of 30
people, including 15 female, each group is to meet a
minimum sample space.
In our exper imen t, contact images were captured in the
condition of volunteer’s hand placed on the bracket. The
bracket indirect under the sensor of image and the dis-
tance was 10 cm. Contact-less images were captured in
the condition of volunteers placed there hand under the
sensor of image 10 cm, parallel his/her hand with the
sensor. The five fingers of the volunteer separate natu -
rally. The image res olution was 1280 × 960 pixels of two
kinds of images.
First of all, Figured 9 character points on the left hand
of every volunteer, just like the Figure 2 showed. Se-
condly, captured 1 image of each volunteer with contact
pattern and named images with An (n = 1···180). Then,
captured 3 images of each volunteer with contact-less
pattern and named them with Bn1, Bn2, Bn3 (n = 1···180)
1) Step 1: Extracted coordinate of 9 character points
of contact image A1 and coordinate of 9 character points
of contact-less image B11 separately. That were point sets
of a1 and b11.
2) Step 2: We accomplished registration from image
B11 to image A1. Coincide the coordinate of 5th point in
image B11 with image A1. Adjusted the middle finger of
image B11 to the middle finger of image A1.
3) Step 3: In the registration image B11, the coord-
inates of 9 points maked up the point set c11.
4) Step 4: We computed the Euclidean distance of the
correspond point in point sets of c11 and the point in
point set a1. Now we computed the 9 Euclidean distance
of 9 charact e r to sum1.
5) Step 5: Followed step 1-4, settle contact-less image
B12, B13 separately, then we get sum2, sum3.
6) Step 6: Ave1 represents the average value of 3
Euclidean distance of 9 points in registration image to 9
points in contact image. The equation of sum is showed
as (2).
7) Step 7: In accordance with this method, the re-
maining 29 samples were completed. We got ave1 ~
8) Step 8: The remaining five groups of samples were
calculated and we got the data.
3.2. Result Analysis of Similarity Experiment
To reflect the variability of 6 sets of data, we calculated a
number of parameters in Table 1, including the sample
mean, the sample variance, the sample standard deviation
and range, the equations as following:
Sample mean:
(5 )
Sample variance:
( )
s xx
= −
Copyright © 2013 SciRes. ENG
Table 1. Similarity statistical parameters of 6 group sample.
Feature Sample
Mean Sample
Median Sample
Utmos t
1 106.95 97.56 1883.28 43.40 186.81
2 107.93 97.38 1410.37 37.55 171.52
3 99.12 91.78 1538.47 39.22 166.49
4 99.77 99.77 1755.89 41.90 158.70
5 99.84 92.64 2173.68 46.62 192.63
6 97.73 89.85 1406.10 37.50 149.96
Sample standard deviation:
( )
s xx
= −
Sample mean in Table 1 varied between 97.73 ~
107.93, varied in 10.2 pixels. Sample median between
the range of 89.85 - 99.77, varied in 10.2 pixels. That
means the sample concentration was good.
Sample variance was a way to reflect the data variabil-
ity of statistical parameters, the larger the sample va-
riance, the data demonstrate the variability of the group
was greater. In the six groups, the sample variance and
standard deviation of the fifth group were maximum,
indicating some of experimenters in this group ha d taken
too far or too near distance from requirements, see the
original picture, we found that two experimenters took
too far distance from the lens, resulting in the median of
the group was small, but the sample variance, sample
standard deviation and sample utmost were great. The
same situation happened in the first group. Other groups
of data indicates the majority of the experimenter could
understand contact-less imaging probably accurate and
the way of contact-less imaging was basically stable.
Directions offset of six groups data was shown in Ta-
ble 2. In order to analyze the above six groups of data,
we draw the surface figure as shown in Figure 7.
In the figure, the x-coordinate was 8 directions, the
y-coordinate was offset and the unit was pixels. The
three-dimensional was six groups of data in the direction
of distribution. Through Figure 7, we could draw a con-
clusion of offset in direction 5-1, 5-3, 5-7, 5-9 were larg-
er than the other four directions. In the direction 5-3 and
5-9, we got the maximum of offset.
3.3. Result Analysis o f Recognition Rate
In the sample space of 30 people, we matched contact-
less image and contact image of every experimenter.
Experiments conducted in-class match 30 times , inter-
class match
times, recognition rate of 99.84%.
The remaining five recognition rates of the sample space
Table 2. basic statistical parameters of 8 directions offset
(unit: pixel).
Direction 5-1 5-2 5-3 5-4 5-6 5-7 5-8 5-9
1 35.1 28.3 37.2 25.9 30.2 38.6 30.5 41.0
2 32.4 20.4 33.7 27.8 29.2 31.2 25.4 35.8
3 35.0 27.6 36.1 24.1 26.0 31.5 26.1 36.5
4 33.9 24.3 32.5 23.4 23.8 32.2 26.6 36.4
5 32.7 23.9 37.5 24.7 25.4 38.5 28.1 34.2
6 32.5 25.8 33.1 24.5 22.5 34.0 25.2 31.0
mean 33.6 25.1 35.0 25.1 26.2 34.3 27.0 35.8
Figure 7. 8 direction offset contrast.
Table 3. Relationship between sample spac e and r ecognition
Space 30
person 60
person 90
person 120
person 150
person 180
Rate 99.8% 98.1% 95.8% 92.8% 90.3% 86.1%
as shown in Table 3.
We did linear regression analysis from TABLEIII and
got the trend line of relationship between sample space
and recognition rate, as shown in Figure 8.
At last, we derived the linear regression equation of
relationship between sample space and the recognition
rate: y = 0.000903 x + 1.0332, the judge coefficient was
R² = 0.9824. Coefficient of determination R² = 0.9824
indicates the explanation power of the regression modal,
that means sample could explained recognition rate di-
versity 98.24%.
4. Conclusions
In this paper, we measured similarity of the contact-less
image to contact image, drew the conclusion of con-
tact-less imaging was basically stable. Scaling and shear
had effect on contact-less image stability. In the 8 direc-
tion of chain code, offset of 5-3 direction and 5-9 direc-
tion were the largest. This paper also derived the linear
regression equation of relationship between sample space
and the contact-less recognition rate in ou r imaging con-
Copyright © 2013 SciRes. ENG
Figure 8. Trend line of relationship between sample space
and recognition rate.
dition. The deviation of contact-less imaging was caused
by the different understanding of imaging situation. The
result we got from this research provided effective and
detailed data to the study of contact-less palm vein rec-
ognition and gave powerful support to contact-less hand
mul ti -feature fusion recognition.
In our future research, we will try to give the curve of
high scope and angle scope that fit for contact-less im-
aging. It will support the contact-less palm vein recogni-
tion and the fusion with the other biometrics feature. At
the same time, the curve of high scope and angle scope
that fit for contact-less imaging will benefit for the other
contact-less biometric technologies, such as hand vein
recognition and palm print recognition.
5. Acknowledgements
This work is supported by National Natural Science
Foundation of China (60972123), Specialized Research
Fund for the Doctoral Program of Higher Education
(20092102110002) and Shenyang Science and Technol-
ogy Devel opment P rogram (F10-213-1-00).
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Relationship between sample space and the
recognition rate
1 (30
3 (90
5 (150
Sample Space
Recognition Rate