A Methodology for Biometric Identification Using Vein ...

International Journal of Contemporary Mathematical Sciences

Vol. 16, 2021, no. 4, 135 – 148

HIKARI Ltd, www.m-hikari.com

https://doi.org/10.12988/ijcms.2021.91610

A Methodology for Biometric Identification Using

Vein Geometry in Infrared Images

of the Back of the Hand

B. Luna-Benoso

Instituto Politécnico Nacional. Escuela Superior de Cómputo. Av. Juan de Dios Batíz,

esq. Miguel Othón de Mendizábal, México City 07738, México

J. C. Martínez-Perales



J. Cortés-Galicia



This article is distributed under the Creative Commons by-nc-nd Attribution License.

Copyright © 2021 Hikari Ltd.

Abstract

With the great boom in technological applications of aspects such as computer security

and access control among others, the need arises to create methodologies capable of

identifying individuals. Identification through biometric features using infrared images

is considered one of the safest modalities within biometrics. In this work, a

methodology is proposed that allows biometric identification from infrared images of

the back of the hand. The methodology consists of 4 modules: 1. Image acquisition, 2.

Image segmentation, 3. Characteristics extraction and 4. Classification. Image

acquisition was carried out through the proposal of an electronic prototype that allows

the capture of infrared images, subsequently the images are segmented to obtain the

136 B. Luna-Benoso, J. C. Martínez-Perales and J. Cortés-Galicia

geometry of the veins by skeletonizing the vascular network of the back of the hand,

to This, methods in the spatial domain and a set of 11 geometric transformations are

used from which characteristics are extracted using Hu moments, finally, for the

classification, the Random Forest model was used. The proposed methodology was

compared with other models via the results obtained by the WEKA platform. The

model yielded an accuracy of 90.20%.

Keywords: Pattern recognition, image analysis, biometrics, random forest.

1. Introduction

The word biometry derives from the Greek bio (life) and metry (measure), from

which the term biometry is inferred as the measure of life [1]. Since its inception, the

field of biometrics has focused on measuring the distinctive features of a person as a

way to authenticate them [2]. The term biometrics is used to encompass different

technologies applied to the identification and authentication of people through their

physical, chemical, behavioral or distinctive features [3]. With the great boom in

technological applications, the need arises to propose methodologies that allow

safeguarding the security, confidentiality and integrity of people's information applied

to electronic commerce and bank transfers, among others [4, 5], in this case, systems

Biometrics are an option for computer security.

There are different biometric systems that make use of different biometric

approaches, some of these approaches are fingerprint recognition [6, 7], iris [8, 9],

facial recognition [10, 11], recognition by signature [12, 13], voice recognition [14,

15], hand geometry [16, 17] and those based on vein geometry [18, 19] among others.

Biometric systems that make use of the geometry of the veins do so using the geometry

of the veins in the fingers [20, 21], the geometry of the veins in the forearm [22, 23],

the geometry of the veins of the palm of the hand [24, 25], or use the geometry of the

veins on the back of the hands [18, 26]. Some works make use of thermal images [27,

28] and others of infrared images [20, 29] for the biometric recognition of the geometry

of the veins.

The main advantages of biometric systems based on the geometry of the veins using

infrared images are that they are non-invasive, they are very precise, difficult to

reproduce, they are non-intrusive, and they are also distinctive and unique to each

individual [30]. On the other hand, infrared light with wavelengths between 740 nm to

940 nm can penetrate the skin tissue up to 5mm in depth, reaching the deoxygenated

hemoglobin, thus forming a dark contrast with the skin tissue [31] showing the veins

as dark lines, from which the geometry of the veins is obtained. Various methodologies

have been developed that allow the identification of people through infrared images of

the veins on the back of the hands, some of these works are as follows. Pal et al. [32]

propose a methodology in which the images are obtained by means of an IR Webcam

Methodology for biometric identification using vein geometry 137

to which an equalization of the histogram and the Hough transform are carried out to

locate the veins in the hand, the extraction of characteristics is done by means of minute

points that include bifurcations and end points, finally to measure the similarity

between two images that allow identification, the Hausdorff distance is used. Park et

al. [33] capture infrared images using a camera equipped with an infrared light emitting

diode, IR filter, mirror and hand support, the image is decomposed into three sub-

images (side view of the hand, back of the hand and vascular pattern) , the extraction

of characteristics includes the thickness of the lateral view of the hand, angles of the

valleys of the fingers, the curvature K and the vascular pattern, the similarity for the

identification is carried out using the Euclidean distance, the distance measured by the

polygon curves and a matching algorithm. Ferrer et al. [34] capture infrared images of

the back of the hand using an array of 21 LEDs with a wavelength of 940nm and a

CCD camera with an infrared filter, then obtain a template of the vein pattern applying

mathematical morphology and Gabor filters, for the identification make use of

similarity by means of Hamming distance. Chen et al. [35] propose a methodology in

which the captured images are made using a CCD camera and an 850 nm infrared LED

matrix. The authors study the difference between signals near prostheses and on the

living body, for identification they combine the method correction, rotation and

translation in conjunction with the PLBP and SVM algorithm. On the other hand, Khan

et al. [36] analyze the factors that affect the biometry of the hand during the capture of

the images, such as the orientation angle of the hand, the distance between the camera

and the light, the color of the skin and the temperature, among others.

In this work, a methodology is proposed that allows the biometric identification of

an individual through the geometry of the veins in infrared images of the back of the

hand. This work is structured as follows. Section 1 shows the introduction, section 2

shows the methodology, section 3 the experiments and results carried out, and section

4 the conclusions.

2. Methodology

Figure 1 (p. 137 below) shows the general methodology used to identify people

through the geometry of the back of the hand. Later each of the modules that compose

it are shown.

2.1.Image Acquisition

The acquisition of the images is the first module that makes up the proposed

methodology, for this, a wooden box measuring 24cm × 22cm × 14cm was built in

black, without the front of the box where the hand is inserted, with a removable top cap

that contains a 3cm radius circumference in the center to allow the passage of the lens

of a 7.2 MP digital camera type Canon PowerShot A470 to which an infrared filter is

attached. On the back face of the box there is also a 3cm circumference where a vein

locator device is secured with infrared LEDs with a wavelength of 850 nm in the shape


of a cylindrical bar where the user places the hand pressing and it emits the Infrared

light through the palm of the hand towards the back, revealing the vascular network on

the back of the hand. Figure 2 shows the image of the prototype that allows the capture

of the images.

Fig. 1. Methodology used for biometric identification using the back of the hand.

Fig. 2. Prototype that allows capturing infrared images of the back of the hand


2.2.Segmentation

A series of passes were carried out that allow the infrared image to be improved

from the beginning until the skeletonization of the geometry of the veins on the back

of the hand was obtained. The steps performed are listed below.

1. Media filter. A 3 × 3 masked media filter is applied to soften the edges or

textures of the image.

2. Weighted average filter. Continuing with the goal of smoothing the image, a

weighted median filter with a 3 × 3 mask is applied with a center value of 2.

3. Cleared the image. A logarithmic stretch is applied to lighten the image such

that pixels with a dark hue are lightened and those with a light hue remain.

4. Grayscale. The grayscale image is obtained by the formula

𝐼(𝑥, 𝑦) = 0.299 ∗ 𝑅(𝑥, 𝑦) + 0.587 ∗ 𝐺(𝑥, 𝑦) + 0.114 ∗ 𝐵(𝑥, 𝑦) where 𝑅(𝑥, 𝑦), 𝐺(𝑥, 𝑦) y 𝐵(𝑥, 𝑦) represent the value of the red, green and blue

plane on the pixel with coordinates (𝑥, 𝑦). 5. Lightened the grayscale image. Logarithmic expansion is applied again to

lighten the grayscale image.

6. Thresholding using Niblack. The Niblack thresholding method is a local type

method, this method is tolerant to changes in lighting. Niblack proposes an

algorithm that obtains local thresholds from a sliding mask 𝑤(𝑥, 𝑦), to obtain

the mean 𝜇(𝑥, 𝑦) and the variance 𝜎(𝑥, 𝑦). The threshold value 𝑇(𝑥, 𝑦) is given

by 𝑇(𝑥, 𝑦) = 𝜇(𝑥, 𝑦) + 𝑘 ∙ 𝜎(𝑥, 𝑦). 7. Morphological Lock and Opening. The morphological operation of the lock is

applied, followed by the morphological operation of the opening with a

structuring element equal to the Moore neighborhood.

8. Image negative. The image is inverted, that is, the white pixels become black

and the black ones become white through the function 𝑁𝑒𝑔(𝑥, 𝑦) = 255 −𝐼(𝑥, 𝑦).

9. Median filter. A median filter is applied to reduce blurring from having

previously applied a median filter.

10. Related components. All components connected through 8-connectivity are

tagged and those with an area smaller than 200 × 200 pixels are removed.

11. Image negative. The image is reversed again.

12. Morphological lock. A morphological lock operation is used with a diamond-

shaped structuring element with a radius of 10 pixels.

13. Morphological dilation. A morphological dilation operation is applied with a

structuring element in the form of a vertical line with a radius of 10 pixels.

14. Morphological lock. A morphological lock operation is applied with a

structuring element in the shape of a circle with a radius of 5 pixels.

15. Morphological skeletonization. The morphological skeletonization process is

applied that allows us to obtain the vascular network of the back of the hand.


Figure 3 shows the application of steps 1 to 15 that make it possible to obtain the

skeletonization of the geometry of the veins of the back of the hand applied to an

example of an infrared image of the back of the hand.

Fig. 3. Segmentation process of the vascular network on the back of the hand. In (a)

original image, after the result of applying in (b) step 1 and 2, in (c) step 3, (d) step 4,

(e) step 5, in (f) step 6, in (g) step 7, at (h) step 9, at (i) step 10, at (j) step 11, at (k) step

12, at (l) step 13, at (m) step 14, at (n) step 15 and finally in (o) the skeletonization of

the geometry of the veins of the back of the hand is obtained.

2.3.Feature extraction

Once the skeletonization of the geometry of the veins on the back of the hand has

been obtained in infrared images as the result shown in figure 3, a total of 11

transformations are applied to the result of the skeletonized image, these

transformations correspond to 45 °, 90 °, 135 °, 180 °, 225 °, 270 ° and 315 ° rotations;

a zoom of 0.85 and one of 1.15; a horizontal reflection and a vertical reflection (figure


4). From each of these images obtained, the 7 Hu moments that are shown below are

extracted.

Φ1 = 𝜂20 + 𝜂02

Φ2 = (𝜂20 − 𝜂02)2 + 4𝜂11

2 Φ3 = (𝜂30 − 3𝜂012)

2 + (3𝜂21 − 𝜂03)2

Φ4 = (𝜂03 + 𝜂12)2 + (𝜂21 + 𝜂03)

2 Φ5 = (𝜂30 − 3𝜂12)(𝜂30 + 𝜂12)[(𝜂30 + 𝜂12)

2 − 3(𝜂21 + 𝜂03)2]

+ (3𝜂21 − 𝜂03)(𝜂21 + 𝜂03)[3(𝜂30 + 𝜂12)2 − (𝜂21 + 𝜂03)

2] Φ6 = (𝜂20 − 𝜂02)[(𝜂30 + 𝜂12)

2 − (𝜂21 + 𝜂03)2] + 4𝜂11(𝜂30 + 𝜂12)(𝜂21 + 𝜂03)

Φ7 = (3𝜂21 − 𝜂03)(𝜂30 + 𝜂12)[(𝜂30 + 𝜂12)2 − 3(𝜂21 + 𝜂03)

2]− (𝜂30 − 3𝜂12)(𝜂21 + 𝜂03)[3(𝜂30 + 𝜂12)

2 − (𝜂21 + 𝜂03)2]

where 𝜂𝑟𝑠 = 𝜇𝑟𝑠 𝜇00⁄ , 𝑡 = (𝑟 + 𝑠) 2⁄ + 1, 𝜇𝑟𝑠 = ∑ (𝑖 − 𝑖)̅𝑟(𝑗 − 𝑗)̅𝑠𝑖,𝑗∈𝑅𝐸𝐺 , 𝑖̅ = 𝑚10 𝑚00⁄ , 𝑗̅ =

𝑚01 𝑚00⁄ , 𝑚𝑟𝑠 = ∑ 𝑖𝑟𝑗𝑠𝑖,𝑗∈𝑅𝐸𝐺 with 𝑟, 𝑠 ∈ ℕ and 𝑅𝐸𝐺 the set of pixels within the region.

Fig 4. In (a) original image, in (b) 45 ° rotation, in (c) 90 ° rotation, in (d) 135 °

rotation, in (e) 180 ° rotation, in (f) 225 ° rotation, in (g) 270 ° rotation, in (h) rotation

of 315, in (i) zoom of 1.15, in (j) zoom of 0.85, in (k) vertical reflection and in (l)

reflection horizontal.

2.4.Clasification

For the classification, use is made of Random Forest whose output is obtained from

a set of decision trees and the decision with the highest votes is the output of the

algorithm. Figure 5 shows the general scheme of the Random Forest algorithm.


Fig. 5. Random Forest Classifier.

3. Experimentation and results

To carry out the experiments, a total of 20 people were considered who were

captured 10 different shots of the back of the hand using the prototype exposed in

section 2.1. The methodology shown in section 2.2 was applied to each of the infrared

images obtained by the users to obtain the skeletonization of the geometry of the back

of the hand, to the result of this skeletonization, the 11 transformations exposed in the

section 2.3 obtaining a total of 2400 images of the skeleton of the geometry of the back

of the hand. To carry out the classification, 80% were used to make up the training set

and the remaining 20% to make up the test set, that is, 1920 images were used to make

up the training set and 480 to perform the tests. The Random Forest algorithm was

implemented, and the WEKA platform was used to make the comparison with other

classifiers. WEKA is a free software platform designed to be distributed under the

GNU-GPL license and used to test machine learning and data mining. WEKA was

written in Java and developed by the University of Waikato, New Zealand. The

algorithms considered to perform the comparison were SVM, MLP, kNN and ID3

decision trees. Table 1 shows the results of the accuracy of each of these algorithms

and is compared with Random Forest.


Classifier Accuracy

SVM 82.91%

MLP 84.37%

kNN 84.37%

ID3 84.37%

Random Forest 90.20%

Table 1. Accuracy shown by various classifiers using the WEKA platform

As can be seen in table 1, with the proposed methodology that allows to obtain the

skeletonization of the geometry of the veins of the back of the hand in infrared images

in conjunction with the proposed transformations from which the Hu moments are

extracted and making use of the classifier Random Forest, the highest accuracy of

90.20% is obtained.

4. Conclusions

The present work shows the proposal of a methodology that allows biometric

identification through infrared images of the back of the hand from which the pattern

of vein geometry is extracted by skeletonization using different algorithms based on

the spatial domain. Once the geometry of the back of the hand has been extracted, it is

proposed to use 11 transformations that are applied to the skeletonized images. These

11 transformations are 45 °, 90 °, 135 °, 180 °, 225 °, 270 °, and 315 ° rotations; a zoom

of 0.85 and one of 1.15; a horizontal reflection and a vertical reflection. The Hu

moments that make up a vector of characteristics of a person for each transformation

are calculated for each of these images. A total of 2400 images of 20 different people

were used for the experiments. The results of the accuracy show that the best

performance is the Random Forest algorithm with an accuracy of 90.20%. In this way,

with the proposal of the methodology from the acquisition of the images, segmentation

and extraction of characteristics, competitive results are obtained using the Random

Forest classifier.

Acknowledgments. The authors would like to thank the Instituto Politécnico Nacional

(Secretaría Académica, COFAA, EDD, SIP and ESCOM) for their economical support

to develop this work.


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Received: August 25, 2021; Published: September 16, 2021

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