Post on 06-Nov-2021
Personal Authentication System using Hand Vein Biometric
G. Sathish*1
Research Scholar, Anna University of Technology,
Coimbatore
Email : gsathishphd@gmail.com
*Corresponding author
Dr. S. Narmadha3
Professor and Head, Dept. of Information Technology,
Hindusthan Institute of Technology,
Coimbatore - 641032.
Email : narmadhasathish@gmail.com
Dr. S.V. Saravanan2
Principal, Christ the King Engineering College
Coimbatore – 641104.
Email : coimbatoreprincipal@gmail.com
Dr. S. Uma Maheswari4
Associate Professor, Dept. of ECE
Coimbatore Institute of Technology,
Coimbatore – 641014.
Email : sumacit@rediffmail.com
Abstract
With an increasing emphasis on security,
automated personal identification using hand vein
biometric feature is becoming a very active topic in
both research and practical applications. The objective
of this work is to present a biometric authentication
system for high security physical access control based
on hand vein pattern. Hand vein biometric is selected,
as it is foolproof, offers higher security and reliable for
identification.
The feature extraction stage consists of removal of
the noise inherent using various filtering techniques
and extraction of the most discriminating information
present in the images using Discrete Wavelet
Transform and Adaptive Thresholding techniques.
The proposed recognition system makes use of
hausdorff distance as matching parameter. The
biometric system developed was tested on WASET hand
vein image database of Chinese Academy of Sciences
Institute of Automation is used and the system is
implemented in MATLAB. The proposed approach
found to report higher verification accuracy of 99.5%.
Keywords- Hand vein, Discrete Wavelet Transform,
Filtering, Adaptive Thresholding, Hausdorff Distance,
WASET
1. Introduction
Recently, security in many situations is receiving
more attention because of increase of crime rate aided
with latest technology which has pressed researchers to
scrutinize for better security provisions. Security has
been important in the view of privacy protection with
the advance of internet technology. Traditional personal
verification methods such as passwords, personal
identification numbers (PINS), magnetic swipe cards,
keys and smart cards offer very limited security and are
unreliable. Consequently, biometrics which involves
the analysis of human biological, physical and
behavioural characteristics has been developed to
ensure more reliable security. Compared to traditional
methods, biometric features are harder for intruders to
copy and forge. Biometric individual authentication is
used in many fields as an approach for security.
Biometrics is the technique of verifying a person's
identity from a physical characteristic (e.g. fingerprint,
handprint, face, scent, thermal image, or iris pattern), or
personal trait (e.g. voice pattern, handwriting, or
acoustic signature). Biometric identification may be
preferred over traditional methods (e.g. passwords,
smart-cards) because its information is virtually
impossible to steal.
Hand vein recognition has more advantages
compared to other biometric feature authentication
technology such as fingerprint, iris, face, and so on.
The advantages of this recognition are as follows:
[1]. Live Body Identification: The hand vein biometric
feature authentication system acquires a vein
image which can be taken only at live body, thus
the vein image at non-live hand cannot be read and
taken, and accordingly no identification and
authentication can be made. Therefore, no
falsification can be done.
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[2]. Internal Features: The hand vein biometric
feature authentication system extracts the vein
pattern inside a hand rather than the outside
features of a hand for authenticating that
person. So there are no barriers for
identification and verification from the
damnification, wear and tear, the dry and the
wet of hand surface.
[3]. Non-Contact: When the hand vein biometric
feature authentication system extracts the vein
pattern, the hand is not in contact with the
device instead hand is just easily stretched and
the capturing of vein pattern is completed. Due
to non-contact, it is hygienic and non-
duplicating and has no negative image
associated with crime.
[4]. High Security: Since the system has got the
three features: live body, internal features and
non-contact, there is no falsification, no barrier
for identification and no misuse by evil-doers,
thus it holds high security grade and can be
used at high level security places
2. Literature Review
Research work on hand vein patterns are
mostly carried out on the palmer part, the dorsal
part and on finger veins. Vein patterns in the
palmer region are made use of by Malki et al. [1].
They have opted cellular neural networks for
feature extraction and reported 99.5%
authentication by direct image comparison.
However, the results are not projected in terms of
false detection error rates. Thermal vein patterns in
the dorsal part [2,3,4] of the hand are extensively
studied by Wang et al. [3,5,6]. Their lucid
presentation on FIR (Far Infra Red) and NIR (Near
Infra Red) imaging methods to capture vein
patterns is illuminating. Using Hausdorff distance
for matching, they have shown 0% FAR (False
Acceptance rate) and FRR (False Rejection Rate)
with a suitable threshold. However their
experiments are carried out on very small database
(only 30 users) thus creating doubts on their
validity to large datasets. Lin et al. [7] have applied
multi-resolution filters that extract dominant points
by filtering out the miscellaneous features from
vein patterns in the hand dorsa. However, their
experimental results indicate a FAR up to 2.3 %
which is considered higher than usually permissible
percentages. Miura et al. [8] are concerned with the
finger vein patterns and irregular shading produced
by thickness of finger bones and muscles using line
tracking operations with randomly varied start
points and achieved equal error rates of 0.145%.
An artificial immune system is developed by
Shimooka and Shimizu [9] based on finger vein
patterns. They introduce ordinal matching and
distribution of bound antibody based strategies for
pattern classification. However, the experimental
results demonstrated on small datasets do not meet
the specifications.
3. Hand Vein Recognition System
The flow of the proposed system is detailed in
the flowchart shown in Figure 2.
Figure 1. Flowchart of the Proposed System
The proposed hand vein recognition system is
composed of three main stages.
3.1 Preprocessing Stage
This stage includes improving the contrast of
the input image by normalizing it in order to extract
the most discriminating features of the hand vein.
The technique used for normalization in this work
is contrast stretching. Contrast stretching fixes
upper and lower limits depending upon the
intensity of the pixels in the image and performs
darkening or brightening of the pixels within these
limits while retaining the pixel values outside the
limits.
a) Image Normalization
The proportion of the vein area varies most of
the time and for convenience dimension size
Contrast Stretching
Input Image taken from
WASET database
2D Wavelet
Transform
Image
Storage
Filtering
Median Filtering
Lowpass Filtering
Highpass Filtering
Adaptive Thresholding
Hausdorff Distance
Calculation
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normalization is done. The vein image is defined as
256×256.
b) Vein Extraction
The quality of images taken by the infrared
collector is low. So, the image segmentation takes
up a very important position in the whole process
of hand vein recognition. Though there are many
image segmentation methods, among them the
classics are threshold method, region growing
method, relaxation method, edge detection method,
division and combination method. An image
segmentation method is adopted chiefly as it is a
local dynamic threshold method simple and
effective. The principle behind the method is that
first the mean and variance of the points in r×r
neighborhood of every point is calculated, then
binarization is performed.
c) Getting Rid of Noises of Binary Image
Noises are wiped off according to their size.
Then the area sizes of the black and white
backgrounds are calculated. If the area size of the
background is smaller than the given size, this
block is considered as noise and is erased.
d) Image Thinning
The Image thinning consists of extracting a
single pixel wide framework for keeping the
topological structure of the original image intact.
The vein image is thinned using the combination
method of general conditional thinning and
templates.
3.2 Feature Extraction Stage
It consists of extraction of features from the
preprocessed hand vein image and also includes
various filtering techniques to remove the inherent
noises from the image and Discrete Wavelet
Transform (DWT) to reduce the size of the image
so as to store the image in the database using lesser
space. Different types of noise like Gaussian noise,
normal noise, Rayleigh noise, impulse noise may
exist in an image acquired using an infrared
scanner. These are removed in this system using
Median filter, Gaussian filter and Spatial Lowpass
and Highpass filters. Additionally, the feature
extraction stage also makes use of Adaptive
Thresholding to extract the vein patterns alone
from the image.
3.3 Recognition Stage Finally, the features of the hand vein pattern of
the input image are compared with those of all
patterns in the database. It makes use of Hausdorff
distance [10] to find the length of the whole hand
vein pattern in the image and thus to find whether
the two hand vein images are same or not.
4. Experimentation Results
The proposed system acquires the input hand
vein image from the disk and based on the choice
provided by the user, decides whether to compress
the image for disk storage or to compare the image
with the templates stored in the database after
performing preprocessing and feature extraction on
them.
Figure 2. Sample Input Handvein Image
taken from WASET Database
If the user wants compression to be performed,
the system uses a 2-Dimensional Discrete Wavelet
Transform (2D DWT) for performing n-level
compression on the image. After wavelet
processing, the template obtained is written to disk.
4.1. Contrast Stretching
If the user‟s choice is comparison, some form
of normalization should be done beforehand in
order to provide „better‟ input for automated image
processing and realize a robust system against
fluctuation. Conventional pre-processing technique
of contrast stretching which darkens pixels in
between two pre-assigned ranges while retaining all
other information is used for this purpose.
The image obtained after contrast stretching is
shown in figure 3.
Contrast stretching (often called normalization)
is a simple image enhancement technique that
attempts to improve the contrast in an image by
„stretching‟ the range of intensity values it contains
to span a desired range of values, e.g., the full
range of pixel values that the image type concerned
allows. It differs from the more sophisticated
histogram equalization in that it can only apply a
linear scaling function to the image pixel values.
As a result the „enhancement‟ is less harsh. Most
implementations accept a gray level image as input
and produce another gray level image as output.
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Before the stretching can be performed it is
necessary to specify the upper and lower pixel
value limits over which the image is to be
normalized. Often these limits will just be the
minimum and maximum pixel values based on the
image type. For example, 8-bit gray level images
have the lower and upper limits „0‟ and „255‟.
The lower and the upper limits can be represented
as „a‟ and „b‟ respectively. The simplest sort of
normalization then scans the image to find the
lowest and highest pixel values currently present in
the image which are represented as „c‟ and „d‟.
Then each pixel P is scaled using the following
function:
Pout = (Pin - c) ((b – a) / (d - c)) + a (1)
Values in between 1 and 2 are set to „255‟ and
the values below 1 and above 2 are retained.
Figure 3. Contrast Stretching
Figure 4. Image Skewing
Figure 5. Image Slicing
4.2 Filtering Techniques i) Lowpass Spatial Filtering
This is a type of smoothing filter used for
blurring. Blurring is used in preprocessing steps,
such as removal of small details from an image
prior to object extraction, and bridging of small
gaps in lines or curves. Noise reduction can be
accomplished by blurring with a linear filter and
also by nonlinear filtering. The filter used for
lowpass (smoothing) spatial filtering would have
all positive coefficients. For the creation of positive
coefficients, the simplest arrangement would be a
mask in which all coefficients have a value of 1.
The impulse response would then be the sum of
gray levels of all pixels divided by the number of
pixels, i.e., the response would be the average of all
the pixels in the area of the mask, the use of masks
for smoothing is called neighbourhood averaging.
ii) Median Filtering
Smoothing filters can be used for noise
reduction, in addition to blurring. If the objective is
to achieve noise reduction rather than blurring, an
alternative approach is to use median filers. That is,
the gray level of each pixel is replaced by the
median of the gray levels in a neighbourhood of
that pixel, instead of by the average. This method is
particularly effective when the noise pattern
consists of strong, spikeless components and the
characteristic to be preserved is edge sharpness.
Median filters are nonlinear in nature.
In order to perform median filtering in a
neighbourhood of a pixel, sort the values of the
pixel and its neighbours, determine the median and
assign this value to the pixel. Thus, the principal
function of the median filtering is to force points
with distinct intensities to be more like their
neighbours, actually eliminating intensity spikes
that appear isolated in the area of the filter mask.
iii) High Pass Spatial Filtering
This is a type of sharpening filter used in the
frequency domain. The principal objective of
sharpening is to highlight fine detail in an image or
to enhance detail that has been blurred, either in
error or as a natural effect of a particular method of
image acquisition. Uses of image sharpening vary
and include ranging from electronic printing and
medical imaging to industrial inspection and
autonomous target detection in smart weapons.
The filter used for highpass (sharpening) spatial
filtering would have positive coefficients near its
centre and negative coefficients in the outer
periphery. For this, choosing a positive value in the
centre location with negative coefficients in the rest
of the mask meets this condition. In a sharpening
filter, the sum of the coefficients is 0. Thus when
the mask is over an area of constant or slowly
varying gray level, the output of the mask is zero or
negligible value. This filter eliminates the zero-
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frequency term, thereby reducing the average gray-
level value in the image to zero, reducing
significantly the global contrast of the image. The
original image consists of fairly fine detail over a
significant portion of slowly varying background
levels. The result is characterized by somewhat
enhanced edges over a rather dark background.
Reducing the average value of an image to zero
implies that the image must have some negative
gray levels. The results of highpass filtering
involve some form of scaling and/or clipping.
Taking the absolute value of the filtered image to
make all values positive is not preferred, because
large negative values would appear brightly in the
image.
Figure 6. 2D Lowpass Filtering
Figure 7. Lowpass Filtering
Figure 8. Median Filtering
Figure 9. Highpass Filtering
The different types of noise that may exist in an
acquired image are removed in this system without
affecting vein pattern regions. Impulse and normal
noise are some of the unwanted data in the image
that are removed using appropriate filters. Spatial
Lowpass, Median filter and Highpass filters are
assigned the function of noise removal. The noise
removal from the input hand vein images are
shown in Figures 6,7,8 and 9 respectively.
4.3 Wavelet Transform
Wavelet is a waveform of effectively limited
duration that has an average value of zero.
Wavelets are functions generated from a single
function by dilation and translation. The
fundamental idea behind wavelets is to analyze
according to scale or resolution. Wavelets are
mathematical functions that split data into different
frequency components, and then study each
component with a resolution matched to its scale.
They have advantages over traditional Fourier
methods in analyzing physical situations where the
signal contains discontinuities and sharp spikes.
Wavelets were developed independently in the
fields of mathematics, quantum physics, electrical
engineering and seismic geology. Interchanges
between these fields during the last ten years have
led to many new wavelet applications such as
image compression, turbulence, human vision,
radar, acoustical analysis and earthquake
prediction.
The conventional Fast Fourier Transform
(FFT) based image denoising method is essentially
a low pass filtering technique in which an edge is
not as sharp in the reconstruction as it was in the
original. But the localized nature of the wavelet
transform both in time and space results in
denoising with edge preservation.
Wavelet transform is used to represent any
arbitrary function as a superposition of wavelets.
It is a form of signal analysis where a signal is of
discontinuities. Wavelet transform is localised in
time and space. It results in denoising with edge
preservation.
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Wavelet analysis is the technique which is
used to perform local analysis, i.e., to analyse a
localised area of a larger signal. Wavelet analysis is
the breaking up of a signal into shifted and scaled
versions or original (a mother) wavelet, because the
signals with sharp changes might be better analysed
with an irregular wavelet than with a smooth
sinusoid. Local features can be described better
with wavelets than sine waves or other waves.
Wavelet transforms are of two types - discrete and
continuous.
A Discrete Wavelet Transform (DWT) is any
wavelet transform for which the wavelets are
discretely sampled. A key advantage it has over
Fourier transforms is temporal resolution: it
captures both frequency and location information.
The DWT holds promise for use in edge detection.
A 2D DWT is used to extract the feature from
the hand vein image and produces four sub bands at
every stage, each one is 1/4th the size of the input
image size to that stage. These four sub bands are
denoted HH, HL, LH, and LL with the first letter
denoting the filter applied horizontally and the
second letter the filter applied vertically (H- High-
pass, L-Low-pass). The first two stages of the 2D
DWT are shown in Figure 10.
Figure 10. First Two Stages of 2D DWT
To obtain next course level of wavelet
coefficients, the subband LL1 alone is further
decomposed and sampled. Each band will be 1/4th
the size of input image size to that image. The input
hand vein image obtained after wavelet transform
is shown in Figure 11.
Figure 11. Wavelet Transform
4.4 Adaptive Thresholding
Thresholding is the simplest method of image
segmentation. From the gray scale image,
thresholding can be used to create binary images.
During the thresholding process, individual pixels
in an image are marked as „object‟ pixels if their
value is greater than some threshold value
(assuming an object to be brighter than the
background) and as „background‟ pixels otherwise.
This convention is known as threshold above.
Variants include threshold below, which is opposite
of threshold above; threshold inside, where a pixel
is labelled „object‟ if its value is between two
thresholds; and threshold outside, which is the
opposite of threshold inside. Typically, an object
pixel is given a value of „1‟ while a background
pixel is given a value of „0‟. Finally, a binary image
is created by coloring each pixel white or black,
depending on a pixel's label. The key parameter in
the thresholding process is the choice of the
threshold value or values.
Several different methods for choosing a
threshold exist; users can manually choose a
threshold value, or a thresholding algorithm can
compute a value automatically, which is known as
automatic thresholding. A simple method would be
to choose the mean or median value, the rationale
being that if the object pixels are brighter than the
background, they should also be brighter than the
average. In a noiseless image with uniform
background and object values, the mean or median
will work as accurately as the threshold specified.
However this will generally not be the case.
A more sophisticated approach might be to create a
histogram of the image pixel intensities and use the
valley point as the threshold. The histogram
approach assumes that there is some average value
for the background and object pixels, but that the
actual pixel values have some variation around
these average values. Thresholding is called
adaptive thresholding when a different threshold is
used for different regions in the image. This may
also be known as local or dynamic thresholding.
Figure 12. Adaptive Thresholding 1
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Figure 13. Adaptive Thresholding 2
Figure 14. Adaptive Thresholding 3
Figure 15. Adaptive Thresholding 4
The vein patterns are extracted after noise
reduction and normalization. This extraction is
done by the process of adaptive thresholding. The
input images after adaptive thresholding are shown
in the figures 12,13,14 and 15.
4.5 Hausdorff Distance
Hausdorff distance is the maximum distance of
a set to the nearest point in the other set. More
formally, Hausdorff distance from set A to set B is
a max-min function, defined as
)b,a(dmaxmax)B,A(hBbAa
(2)
where a and b are points of sets A and B
respectively and d(a, b) is any metric between these
points.
Figure 16. Hausdorff Distance Calculation
The extracted vein patterns of the input image
can directly be compared with the templates.
Hausdorff distance defined to calculate the
similarity between the template and the input
patterns has been adopted as matching algorithm in
for this system and the output of the distance
calculation is shown in Figure 16.
5. Result Analysis
Hausdorff distance was applied to the vein
features. A genuine versus imposter distribution
chart was used for vein pattern matching to deduce
the false acceptance rate (FAR) and the false
rejection rate (FRR). False Acceptance Rate refers
to the total number of unauthorized persons getting
access to the system over the total number of
people attempting to use the system. False
Rejection Rate refers to the total number of
authorized persons not getting access to the system
over the total number of people attempting to get
access to the system. The y-axis coordinate shows
the image number and gives an idea about the total
number of hand vein images in the database.
The x-axis coordinate plots the hausdorff distance
selected as threshold for the particular image.
Figure 17. Genuine versus Imposter
Distribution
The distribution chart shown in Figure 17 plots
the different threshold values applied to find an
optimal value. Values from 1 to 75 were used for
this purpose. Imposter vein patterns were falsely
assumed to be valid for vein images when the
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threshold was fixed between 32 and 37. Likewise,
genuine images were rejected at times when the
threshold was fixed between 37 and 42. These
ranges give the range of the FAR and FRR
respectively. The coincidence of genuine and
imposter values occur at 37 and so it was fixed as
the threshold for distance calculation using
Hausdorff distance.
In Table 1 the threshold values for Genuine and
Imposter distributions are tabulated.
Table 1. Thresholds Distribution
Image
No.
Threshold
value for
Genuine
Distribution
Threshold
value for
Imposter
Distribution
5 2 40
10 5 42
15 7 45
20 10 47
25 12 50
30 15 52
35 17 55
40 20 57
38 22 60
33 25 62
28 27 65
23 30 67
18 32 70
13 35 72
8 37 75
Experimental results show that the method is
effective and feasible with refusing recognition
ratio up to 99.5%.
6. Conclusion and Future Work
In this work, a procedure for hand vein feature
extraction has been developed based on DWT,
Adaptive Thresholding and Filtering techniques.
This paper presents a hand vein recognition system
tested using hand vein images from WASET
(World Academy of Science, Engineering and
Technology) database. The similarity between the
images is calculated using Hausdorff Distance
(HD). It is found from the experiments that the
Hausdorff Distance between two hand vein images
that belong to the same hands is 37.
Based on the experimentation results, it can be
concluded that the procedure proposes a reliable
and accurate system for hand vein identification.
The use of filters produces an uncorrelated and less
redundant representation for hand vein texture
compared with other techniques. The method
achieved correct tracking of features of hand vein
images using adaptive thresholding. Moreover, it
produces very high identification results that are
suitable for methodical search in large databases
and access control.
Future directions of vein recognition
technology include advanced use of hand vein
recognition systems for government and military
security, justice and law enforcement, healthcare,
border control/airports, financial and transactional
security.
The personal identification technique has been
tested only for the WASET database image hence
testing more samples of hand vein images could
extend the study. Large shareable large databases
should be founded for a thorough evaluation on the
efficacy of different vein recognition algorithms.
Handvein images can be used as biometric
watermark for securing digital images on which the
authors are currently investigating and have
reported improved results [11]. Further, hand vein
biometric can be used to watermark images in
fusion with other biometric to build a more robust
multimodal high secure authentication system.
References
[1]. S. Malki and L. Spaanenburg, “Hand Veins
Feature Extraction using DT-CNNS”, Proc.
SPIE, Vol. 6590, pp. 65900-165900, 2007.
[2]. Z. Hong, L. Han, X. Wei, and D. Yingna, “Seal
Imprint Verification based on Multi
Supplemental Features of Multi-Classifier
Fusion Decision”, Department of Computer
Engineering and Applications, Harbin
Engineering University, Harbin, China,
Vol. 34, pp. 215-217, 2004.
[3]. A.K. Jain, S. Prabhakar and S. Pankanti,
“Online Fingerprint Verification”, IEEE Trans.
Pattern Analysis and Machine Intelligence,
Vol. 19, pp. 302-314, 2000.
[4]. L. Wang and G. Leedham, “A Thermal Hand
Vein Pattern Verification System”, Lecture
Notes in Computer Science, Vol. 3687, No. 10,
pp. 58-65, 2005.
[5]. A.K. Jain, A. Ross and S. Prabhakar, “An
Introduction to Biometric Recognition”, IEEE
Trans. on Circuits and System for Video
Technology, Special Issue on Image and Video-
Based Biometrics, Vol. 14, No. 1, pp. 4-20,
2004.
[6]. L. Wang and G. Leedham, “Near- and Far-
Infrared Imaging for Vein Pattern Biometrics”,
IEEE Int. Conf. on Video Based Surveillance,
Sydney, Australia, pp. 52-52, 2006.
[7]. C.L. Lin and K.C. Fan, “Biometric verification
using Thermal Images of Palm-dorsa Vein
Patterns”, IEEE Trans. on Circuits and Systems
for Video Technology, Vol. 14, pp. 199-213,
2004.
[8]. N. Miura, A. Nagasaka and T. Miyatake,
“Feature Extraction of Finger Vein Pattern
based on Repeated Line Tracking and its
Application to Personal Identification”,
G Sathish et al ,Int.J.Computer Technology & Applications,Vol 3 (1), 383-391
IJCTA | JAN-FEB 2012 Available online@www.ijcta.com
390
ISSN:2229-6093
Machine Vision and Applications, Vol. 15,
pp. 194-203, 2004.
[9]. T. Shimooka and K. Shimizu, “Artificial
Immune system for Personal Identification with
Finger Vein Pattern”, Lecture Notes in
Computer Science, Vol. 3214, pp. 511-518,
2005.
[10]. Y. Gao and M.K.H. Leung, “Line Segment
Hausdroff Distance on Face Matching”, Pattern
Recognition, Vol. 35, No. 2 , pp. 361-371,
2002.
[11]. G. Sathish, S.V. Saravanan, S. Narmadha and S.
Uma Maheswari, “A Robust Biometric dual
Watermarking Technique with Hand Vein
Patterns for Digital Images, Int. J. Biometrics,
Vol. 3, No. 2, pp. 159-174.
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