Personal Authentication System using Hand Vein Biometric

Personal Authentication System using Hand Vein Biometric

G. Sathish*1

Research Scholar, Anna University of Technology,

Coimbatore

Email : [email protected]

*Corresponding author

Dr. S. Narmadha3

Professor and Head, Dept. of Information Technology,

Hindusthan Institute of Technology,

Coimbatore - 641032.


Dr. S.V. Saravanan2

Principal, Christ the King Engineering College

Coimbatore – 641104.


Dr. S. Uma Maheswari4

Associate Professor, Dept. of ECE

Coimbatore Institute of Technology,

Coimbatore – 641014.


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.

G Sathish et al ,Int.J.Computer Technology & Applications,Vol 3 (1), 383-391

IJCTA | JAN-FEB 2012 Available [email protected]

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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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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

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Personal Authentication System using Hand Vein Biometric

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