Human Authentication Based On Dorsal Hand Veins : A ReviewNatarajan presented an image descriptor which uses statistical structure of natural images. The image was converted into integer

Human Authentication Based

On Dorsal Hand Veins: A

Review

Nisha Charaya

Asst. Professor, ECE Deptt.

Amity University Haryana

Gurgaon,Haryana

[email protected]

Priti Singh

Professor, ECE Deptt.

Amity University Haryana

Gurgaon,Haryana

[email protected]

abstract— Security has become an integral part of our lives today with people having numerous accounts and carrying out high value transactions. Biometrics is increasing-ly being preferred for security, owing to its uniqueness and being difficult to replicate.

Vein recognition is one of the fast growing technologies in this field due to its own unique characteristics and benefits. Veins present in retina, face and hand can be used for personal authentication. Hand veins are drawing attention due to ease in acquisi-

tion and also difficult to forge hand vein pattern. A lot of techniques have been used in this domain. In this paper, we present a survey of the various existing techniques with their performances.

Keywords— Biometric; hand veins; authentication; fuzzy vault; local matching; wavelet; thresholding; filter bank.

Introduction Hand vein patterns refer to the vast network of blood vessels that is present under

a person’s skin. The vein pattern hidden under the skin is quite different in persons, even

for identical twins and remains stable over long period of time. The blood is carried from

one part of the body to another by the veins and therefore vein network is extensively

spread all over the body. The veins present in hands, i.e. finger, palm and dorsal hand sur-

face, can be acquired easily (using near infrared light) and have been used for the biometric

authentication and identification [10].

A lot of research has been done in last two decades in this direction. A sensor for

dorsal hand vein has been presented by Wang and Leedham which uses near-

infrared illumination at 750nm. To achieve images of good quality, near infrared

illumination is angled so that there is a regular illumination on the dorsal hand sur-

face [2]. A digital single-lens reflex camera is used along with a position rod

International Journal of Pure and Applied MathematicsVolume 119 No. 16 2018, 2175-2185ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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which clutches camera and NIR illuminator [3]. To obtain images with a good

contrast, the lightening setup is controlled using an infrared filter in conjunction

with the diffusing paper. Im et al. also used a similar type of image capturing de-

vice with digital single-lens reflex camera and a metallic rod which can hold hand.

The near infrared illumination is obtained from two different LED lamps radiating

light of wavelength 850 nm. The images of resolution 3488 × 2616 pixels are cap-

tured. Thus from the study, it can be concluded that near infrared based camera is

preferred to acquire the vein patterns from dorsal hand regions [4].

Lin and Fan have acquired palm dorsal images using infrared (IR) cam-

era based on thermal imaging and investigated the personal verification. The ap-

proach presented was fully automated and used the multi-resolution representa-

tions for representing the thermal vein patterns obtained after processing [1].

Wang and Leedham also worked on thermal hand vein images but used another

approach for personal authentication [2]. Authors have demonstrated Hausdorff

distance as a pattern matching strategy to compare the extracted vein patterns with

the registered patterns and achieved good results[2], [31]. After this, a new image

acquisition technique was developed using near IR radiation having wavelength in

the range of 780–1100 nm. Its functionality was based on the property that in this

range, there is less absorption of radiation by veins as compared to the surround-

ing blood and, therefore, images with high contrast are obtained. Cross and Smith

presented this imaging technique for the extracting the vein pattern from hand.

Authors have verified the authentication of 20 users by using the two-fold match-

ing in medial axis representation, followed by the extraction of vein skeleton [3].

Im et al. have extracted hand vein using FPGA and DSP processor but with less

details about the technique used for matching patterns and on the database used

for the authentication purpose [4], [5]. Tanaka and Kubo used a phase correlation

scheme based on FFT for user verification on the near IR images of hand veins

[5]. Personal authentication has also been reported by using conjunctival vascula-

ture pattern [5]. The far infra-red based imaging cameras are greatly affected by

surrounding conditions and very costly. Therefore, most of the researchers are us-

ing near IR imaging technique for image acquisition. However, the translation and

rotation of images greatly affect the results. To overcome these, the imaging setup

in prior work used hand docking frame device which restricts the translation and

rotation of hand that makes it tiresome and is not user friendly [3], [4], [5], [4],

[5]. M. Akram, H. Awan and A. Khan used a method based on filter bank for en-

hancement of hand veins. They used a green channel, Gabor filter for image en-

hancement and segmentation was done using supervised multilevel thresholding.

Crossing number method & windowing technique was used for feature extraction

and validation respectively [15].

Kumar and Prathyusha presented a fully automated authentication system incorpo-

rating triangulation of hand vein images and knuckle tips information. The knuck-

le tips were utilized for normalization of the image and extracting region of inter-

est [10]. Yuskel, Akarun and Sankur used a different approach which combined

techniques based on geometric attributes and appearance of hand. They used ICA

i.e. independent component analysis and NMF i.e. non-negative matrix factoriza-

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tion methods for feature extraction. Moreover, they presented the effect of stress

and temperature variation on the performance [24]. Anantha, Premalatha and

Natarajan presented an image descriptor which uses statistical structure of natural

images. The image was converted into integer labels using filters obtained from

stack of natural images.These labels were converted into histogram and used for

further image analysis [21].

However, to increase the template security, V. Brindha used fuzzy vault. An input

key along with the features extracted through binarization and thinning form the

fuzzy vault. The stored fuzzy vault, when combined with biometric template from

dorsal hand vein images, generated the final key for decoding [23]. Y. Tang, D.

Huang and Y. Wang used multi-level key point detection and scale invariant fea-

ture transform based local matching. The detection technique derived by Harris-

Laplace and Hessian-Laplace detectors has been used for localizing sufficient key

points to obtain more distinct information. Then these key points between hands

dorsa of the same individual are efficiently related by SIFT based local matching

[18]. They further extended it by including optical characteristics of the skin that

are person dependent and encoded geometrical attributes of their landscapes. They

developed an effective key point detection strategy based on modeling specialty of

scattering and absorption of the entire dorsal hand. This strategy was adopted to

extract features from dorsal hand images. These features comprehensively de-

scribed the each dorsal hand [19].

Fig 1: Block Diagram of Hand Vein Recognition System.

METHODOLOGY

The framework for dorsal hand vein based authentication system comprises of four modules: image acquisition, image pre-processing & feature extraction, pattern matching and the authentication module as shown in fig 1.

Image Acquisition

The hand vein pattern can be captured by two techniques using infrared ray; the

far-infrared (FIR) based imaging and the near-infrared (NIR) based imaging,


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which can capture human body’s images without causing any harm or discom-

fort.

Far-Infrared based Imaging

The vein pattern present in hand gets thermally mapped into the charge coupled

device camera when far infrared ray is passed through the hand kept at normal

room temperature, as shown in Fig 2.This happens because the human vein sur-

face is at a higher temperature as compared to the surrounding parts. This method

produces good results for the dorsal surface of hand as compared to the palm sur-

face. The dorsal hand vein image captured so, is of fine quality having more use-

ful information, but palm and wrist vein images captured by FIR have poor quali-

ty [25].

Fig 2: FIR Based Images of Dorsal Hand Vein [25]

Near-Infrared based Imaging

The wavelength of NIR lies in the range of 700 nm to 1400 nm. The deoxidized

haemoglobin present in veins completely absorbs near infrared ray, which makes

them appear darker as compared to the rest of image as shown in Fig 3. It has

been adopted by most of the researchers for capturing vein images.

Fig 3: NIR Based Images of Hand Vein of Dorsal Hand, Palm, Wrist, And Finger Vein [25]

Near infrared technique is temperature independent, so the result from this tech-

nique remains unaffected by the surrounding temperature. But in the case of imag-

es acquired by FIR based method, the quality of images is greatly affected by sur-

rounding temperature. So, NIR method is preferred for acquiring all types of hand

vein images [25].

The near infrared based imaging is a commonly used approach in the prior works

that provides fine quality images of vein patterns present in dorsal hand. Howev-

er, initially the researchers started with acquisition using a CCD camera and ther-

mal imaging system.


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Image Pre-Processing & Feature Extraction

Image pre-processing includes region of interest selection, noise removal, contrast

enhancement followed by image segmentation. To make the system more accurate

and reliable, it is essential that features describing vein pattern are extracted from

the same region within different vein images. This process is known as extraction

of region of interest. It then saves this ROI for further image processing steps. ROI

selection can be done based on some property related to appearance of hand like

hand geometry, hand contour, knuckle points etc [32].

Illumination affects the quality of images and due to which a noisy and blurred

image is acquired. To remove the noise from image, a filtering system is applied.

An image filtering system detects and removes the noisy pixels in the image. Dif-

ferent techniques are available for improving the quality of images, as the filtering

methods remove noise from the image; contrast enhancement enhances the con-

trast of the image and segmentation method separates the foreground image from

the background [11].

After this, pre-processing algorithms are applied to extract the vein pattern from

the normalized, noise-free image. The accuracy of system depends directly on the

information extracted from veins. So, to develop a reliable authentication system it

is desirable that the vein patterns are appropriately processed and extracted. The

various algorithms used for vein patterns extraction can be categorized into four

major categories: tracking method, transform method, matched filter method and

threshold method.

Tracking Method

In the tracking based method, the key concept is to track the vein repeatedly start-

ing from the base point in the preprocessed near infra-red image as shown in Fig

3. The position of the deepest point in the cross section determines the direction of

tracking. This is started simultaneously at various locations. The process starts

with identification of local dark lines and after that, by moving pixel by pixel

along the line, it gets tracked. Tracking operation is started again at some other

position when a dark line is not detected. By repeatedly performing this tracking

operation, all the dark lines in the image gets tracked. Finally, the pattern of hand

veins is obtained using statistical approach by overlapping the loci of the lines.

Due to repeated tracking of the parts of the dark lines, they are increasingly em-

phasized. This method have been successfully implemented on hand veins to

achieve robust pattern extraction and resulting in an equal error rate of 0.145% in

individual identification [21]. This method has the advantage that it can extract the

vein pattern from the low quality images, but it is highly affected by the gradual

change of widths of veins.


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Fig 4: Tracking Method [25].

Transform Method

In this method, the image is transformed from spatial domain to wavelet domain,

and wavelet coefficients are used to represent the grayscale image as shown in Fig

5.The wavelet representation of grayscale image contains wavelet coefficients for

both; the vein patterns and noise.

Fig 5: Transform Method (i) Spatial Domain (ii) Wavelet Domain [25]

A wavelet transform is used for it which is an extension of traditional Fourier

transform. There two big classes of wavelet transforms - continuous and discrete;

of which discrete being preferred for its discrete nature. The discrete wavelet

transform (DWT) is a multi-resolution transform that gives time and frequency in-

formation. DWT decomposes the image into high frequency components and low

frequency components. In veins, high frequency information is related to noise.

Hence, low frequency information is used to enhance the quality of the images and

the overall performance. Wavelet decomposition depends on a single wave called

the mother wavelet, it can be also considered as band pass filter. High pass filter

produces the detailed components of the image, while the low pass filter produce

the coarse approximate of the image [24].

Daubechies, Haar and Bior are the generally used wavelets. Chen et al. used wave-

let in conjunction with linear discrimination analysis (LDA) and taking nearest

neighbor classifier. The performance of these three has been compared for veins.

The results of level -1 and level -2 decomposition are better than level three and

above. This can be justified by the loss of information in the decomposition pyra-

mid since only the approximate image is used to obtain the next level. It is con-

cluded that Daubechies and Haar wavelets yield superior performance than Bior


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wavelets. This is due to the asymmetric and orthogonal properties of these wave-

lets which are important for perfect image decomposition and reconstruction [24].

Matched Filter Method

The vein pattern is extracted by adopting a matched filter while a Guassian shaped

curve is used to present the gray-level profile of the cross section is presented as a

Gaussian shaped curve. A filter bank is generated from a set of cross sectional

profiles in equiangular rotations since the vein patterns may appear in any orienta-

tion. The method based on filter bank has been used for dorsal hand veins and sys-

tem has resulted in false acceptance rate of 1.3% and false rejection rateof1.75%

[15]. Brindha used matched filter to yield 90% results [23]. Fig 6 shows the fil-

tered image obtained from filter bank and matched filter.

(a) (b)

Fig 6: Filtered Image from (a) Filter Bank [40] (b) Matched Filter [23]

Thresholding Method

Thresholding is used to represent shapes of the vein patterns in a better way. In the

NIR image of veins, the intensity values vary over different locations. Hence, it is

inappropriate to apply a single global thresholding for the whole image. Different

threshold values can be chosen for every pixel in the image via adaptively adjust-

ing local thresholding. The vein patterns are separated from the background after

analyzing its surrounding neighbors and the desired vein image is extracted [25].

Different thresholding techniques have been used by the researchers. Otsu’s

thresholding has been used in [25] with K-means clustering algorithm for segmen-

tation and ICA for feature extraction to yield 0% FAR and 20% FRR. Brindha im-

plemented local thresholding in combination with Hough transform for feature ex-

traction and K- nearest neighbor for pattern matching [23].

Pattern Matching

Pattern matching refers to the comparison of input test image and the images reg-

istered during the enrollment phase with respect to the extracted vein patterns. The

patterns can be represented by different features like the number of intersections,

length of the total segment, longest segment length, and the angles found in the

image, vein distribution, and other statistical features. A certain distance is then

selected to calculate the similarity between the stored template and the input vein

pattern. Hausdorff distance is commonly used as matching algorithm which is de-

fined as the greatest of all the distances from a point in one set to the closest point

in the other set. It calculates the similarities between the two patterns: extracted


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feature pattern and the registered pattern in a database. Informally, two sets are

close in the Hausdorff distance if every point of one set is in proximity with some

point of the second set [2], [23], [24].

Authentication Module

There are two phases for authentication process: Enrolment phase and testing phase.

During user enrolment, a new user labeled with its identity is registered in the database

along with its feature set. In testing phase, the test user is compared with the enrolled users

called as templates and match score is generated. This match score is further used to either

determine or verify the identity of an individual [11].

For the decision making part, either a global threshold or a local threshold for each

image can be used. According to literature survey, an adaptive local threshold

gives better results due to high variability in the patterns of the individuals. It finds

the minimum distance or maximum similarity from an image in the database and

returns the individual identity of that image and matches it to the test sample.

If the extracted feature between all the samples and the test samples exceeds a cer-

tain threshold value, the user is declared unidentified [25].

CONCLUSION Hand vein being a unique, stable, anti-forgery, contactless technique, is seeking

attention for use as a biometric. Its hardware implementation can be used for large

number of real life applications which gives more reliability; high accuracy and

security. In this paper, the different approaches for image acquisition, feature ex-

traction, pattern matching and the authentication module are studied. The paper

presents the general methodology and key techniques available for dorsal hand

vein technology. By using appropriate techniques for acquisition, pre-processing,

feature extraction, pattern matching and authentication module its performance

can be further enhanced. From the study, it is found that NIR imaging technique is

the best for hand vein image acquisition. Dabuchies wavelets up to level 2 are

suitable for feature extraction. Hausdorff distance is good for pattern matching and

adaptive local thresholding is appropriate for authentication. Moreover, it can be

extended/ modified to overcome translational and rotational effects to make it

more user-friendly.

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Human Authentication Based On Dorsal Hand Veins : A ReviewNatarajan presented an image descriptor which uses statistical structure of natural images. The image was converted into integer