Perfo rmance Evaluation of Multimodal Biometrics SystemBiometric authentication has attracted...

Performance Evaluation of Multimodal

Biometrics System 1A.S. Raju and

2V. Udayashankara

1Department of Electronics & Instrumentation Engineering,

Sri Jayachamarajendra College of Engineering,

Mysore, India.

[email protected] 2Department of Electronics & Instrumentation Engineering,

Sri Jayachamarajendra College of Engineering,

Mysore, India.

[email protected]

Abstract Biometric authentication has attracted several researchers due to its

importance in security applications. Literature lists various unimodal and

multimodal authentication systems. In this work, a multimodal biometric

system is presented that makes use of Electrocardiogram (ECG), Face

recognition and Fingerprint traits which is robust to spoof attack and

liveliness detection. Face and Fingerprint feature extraction are computed

by Central Symmetric Local Binary Pattern (CS-LBP) and Local Binary

Pattern (LBP) respectively. Amplitude and interval features are selected for

ECG recognition. A multimodal biometric database with face, fingerprint

and ECG biometric features has been collected for 50 users and the

biometric system is built using feature level fusion. The trained fused (ECG,

Fingerprint and Face) multi-biometric features are compared with the test

features using Euclidean distance for authentication. Experiments on the

acquired database of ECG, Face and Fingerprint recognition yields False

Acceptance Ratio (FAR) and False Rejection Ratio (FRR) values

significantly better compared to unimodal authentication system.

Index Terms:Feature selection, ECG, fingerprint, face recognition,

multimodal biometric system.

International Journal of Pure and Applied MathematicsVolume 118 No. 5 2018, 367-382ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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1. Introduction

An individual can be automatically recognized using biometric technology

based on the behavioral and biological characteristics. A biometric

characteristic can be either behavioral or biological property of an individual.

By using distinguishing and repeatable biometric features automated

recognition of individual can be achieved. For example, fingerprint, face

recognition, iris recognition etc. Biometric features are stored for the purpose of

comparison in the form of templates. During the recognition process, the actual

biometric is compared with the stored template.

There are two main steps in biometric authentication,

Identification process: The biometric features are compared with several

stored biometric traits.

Verification: The biometric features compared with only one biometric

trait stored in the system.

Identification and verification become equivalent if single biometric trait is

stored in the system. Otherwise, biometric verification process is a limited

version of biometric identification.

Human physiological and/or behavioral characteristics can be used as biometric

features if it satisfies the following criteria.

Universality: The Universality requirement refers to any physiological

or behavioral characteristic that every individual should have.

Distinctiveness: The Distinctiveness refers to any two persons should

sufficiently differ in characteristic.

Permanence: The Permanence requirement refers to the characteristic

that should be sufficiently in-variant over a period of time.

Collectability: The Collectability refers to the characteristic that should

be measurable.

The human identification and verification can be achieved by an important

factor - Biometric Recognition. There are already various biometric traits

presented in today’s security applications, but not all of them are used for high

security applications. The most widely used biometrics is also prone to

inaccuracies and can cause falsification. This paves the way for a need of novel

biometric recognition. The Multimodal Biometrics System can handle multiple

physiological or behavioral characteristics for identification, verification or

enrollment. Some forms of biometric identification [1] include the following.

Fingerprint.

Face geometry.

Iris.

ECG (Electrocardiograph).

EEG (Electroencephalograph).

Voice print.

International Journal of Pure and Applied Mathematics Special Issue

368

Blood vessel patterns in Hand or Retina.

Signature/Handwriting Dynamics.

Finger geometry.

Here, a novel approach for human identification and verification based on ECG

along with Fingerprint and Face of an individual is proposed. This work

explores the effectiveness of individual ECG biometric with other two eminent

biometric traits i.e., fingerprint and face which are known to be a least

conspicuous for efficient individual authentication. Unimodal biometric system

is neither secured nor can it achieve the optimum performance. However,

combining three different modalities of biometrics, offer advantage for user

authentication [2].

2. Various Biometrics: Ecg, Fingerprint & Face

This section gives the details of various basic parameters related to individual

biometrics traits.

ECG

The illustration of electric potentials which are responsible for the normal

functioning of heart activity and its various parameters leads to ECG, in which

the main bioelectrical activity happens by the functioning of cyclical

contractions and relaxations of the heart muscles. An average cycle of ECG

yields the particular waves or parameters with respect to atrial or ventricle

depolarization and/or repolarization. The most prominent bioelectric features of

an ECG show the evidence lying in the P, Q, R, S and T waves. The subject

shows the dissimilar patterns in their ECG signals, because of change in

individual morphology, time duration and range of amplitudes with respect to

their heartbeats. The uniqueness of ECG signals within the individuals happens

for the reasons like dissimilarity in size of heart muscle, position, and physical

state of their heart.

Figure 1 gives the details of standard ECG signal and its parameters; it indicates

physiological signal with its inherent feature of liveliness that signifies the life

signs. The feature of ECG guarantees an individual to be present in person at

the time and place of enrollment. Thus, the use of ECG signal for biometric

purpose is resistant to spoof attack and also ensures the robustness in biometric

system. It is mimic proof and hard to replicate or stolen. Therefore, the ECG has

the tough credential to successfully handle the privacy and security issues of an

individual [3].

Pre-Processing

In the view of signal analysis, pre-processing of ECG signal is important. Its

aim is to suppress the noise and artifacts present in the signal. The ECG signal

when acquired will be mixed with the 50 Hz interference signal. This leads to


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error in feature values when not removed. Hence there is a need for pre-

processing

Feature Extraction

The features in an ECG signal are many. Here, the amplitude, angle and few

interval features are estimated. The heterogeneity of ECG signals among

individuals can be due to the variation in size, position and physical condition of

their heart. Hence, for a particular person these features are constant.

Amplitude Features

The pre-processed ECG signal is applied with wavelet transform. A window of

certain time period is considered and the highest peak in it is identified. R peak

holds the maximum amplitude in the ECG signal and is marked as R peak.

Figure 1: Standard ECG Signal Parameters

The R peak location is recorded and is preserved as Rloc (location of R

peak).Similar procedure is repeated to all the cycles of the ECG sample and R

peak, Rloc values are stored.

The P peak is available before R peak in the time slot of 50-200ms. By using

window, the peaks are analyzed in respective time intervals. The location

corresponding to P peak is stored as Ploc. In the similar manner Q, R, S and T

are also extracted. The waves extracted from the ECG are marked on the ECG

signal [4], [5].

Angle Features

The angle subtended at the peaks in ECG signal can be utilized for the purpose

of biometric recognition. Here three such angle features are used: angle PQR,

angle QRS and angle RST. Mathematical concepts of finding the angle between

two lines are used to calculate the angle. [6].


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Table I: ECG Amplitude Features

ECG

Signal

R

Peak

Q

Pea

k

S

Peak

T

Peak

P

Peak

ECG 1 0.701

4

0.04

15

0.168

0

0.015

4

0.073

6

ECG 2 0.356

9

0.08

67

0.326

4

0.326

4

0.046

0

ECG 3 0.607

8

0.04

60

0.100

7

0.181

6

0.063

9

ECG 4 0.696

1

0.05

40

0.142

0

0.068

0

0.039

8

ECG 5 0.615

0

0.02

29

0.068

6

0.071

6

0.222

0

Table I illustrates ECG signal parameters such as P, Q, R, S and T Peak

amplitude features and its values, for ECG samples from acquired database.

Interval Features

These are another set of features that can be used for Biometric recognition.

ECG possesses several interval features. These features are at peak to peak

intervals, namely, RP, RQ, RS, RT and RR intervals [7].

Table II: ECG Interval Features

ECG

Signal

QRS

Interval

P-P

Interval

P-R

Interval

R-R

Interval

Q-T

Interval

ECG 1 0.0936 0.8648 0.9834 0.8632 0.3026

ECG 2 0.1270 0.8482 0.9959 0.8533 0.3619

ECG 3 0.0692 0.8804 0.9927 0.8774 0.3681

ECG 4 0.0716 0.9151 1.0254 0.9142 0.3667

ECG 5 0.0475 0.6739 0.7952 0.6740 0.3538

Table II illustrates ECG signal parameters such as QRS, PP, PR, RR, QT

interval features and its values, for ECG samples acquired from our database.

Table III: ECG FAR/FRR/TSR Values

Threshold FAR FRR TSR

0.01 29.1667 41.6667 58.3333

0.1 37.5000 25.0000 75.0000

10 43.7500 12.5000 87.5000

40.182 43.7500 12.5000 87.5000

100 43.7500 12.5000 87.5000

500 45.8333 8.3333 91.6667

1000 50.0000 0 100.0000

Table III illustrates ECG accuracy parameters such as false rejection rate, false

acceptance rate and Total success rate values with respect to different threshold

value, for ECG samples from acquired database. To verify the performance of


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ECG based authentication system, angle and interval features are extracted from

the ECG samples selected randomly from database. The algorithm is tested on

all the three quantitative measures (FAR, FRR and TSR), various performance

parameters are considered and computed. The FRR, FAR and TSR are

computed to determine the effectiveness of the proposed algorithm which is

good.

Figure 2 depicts the graphical representation of FAR and FRR values for

acquired ECG database. It indicates the threshold values against to error % of

the ECG parameters. It can be observed that the FAR and FRR values for ECG

biometric authentication are better in real time database. Table 4 shows the

performance results of ECG algorithm, which depicts all the values of various

performance parameters for the acquired ECG database. Accuracy, sensitivity

and specificity are also good. Hence this method can be used in real time

biometric recognition systems.

Figure 2: FAR/FRR for ECG Database

Table IV: ECG Accuracy Parameters

1 Accuracy 70.8333

2 Sensitivity 68.8889

3 Specificity 74.0741

4 Positive Predictive Value(PPV) 81.5789

5 Negative Predictive Value(NPV) 58.8235

6 False Positive Rate (FPR) 25.9259

7 False Discovery Rate (FDR) 31.1111

8 False Negative Rate (FNR) 18.4211

Fingerprint

Fingerprint represents the feature pattern of a finger. With evidence, it is

strongly believed that every fingerprint is unique. The manual classification of

fingerprint is time prone to errors and time consuming. The very first scientific

paper related to fingerprint recognition was published in 1864 and the first

automatic fingerprint identification system (AFIS) was introduced in 1991.

Since then, there is a rapid progress has been made in recognition rates [8].


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Figure 3: Fingerprint Image

Figure 3 shows a standard fingerprint representation. It consists of ridges and

furrows. These ridges and furrows present good similarities in each small local

window, like parallelism and average width between ridges. However, literature

indicates that, fingerprints are not distinguished minutia (abnormal points on the

ridges), not by ridges and furrows. A variety of minutia is presented in

literature. Among them, two are most significant and is used to larger extent:

Termination represents the immediate termination of ridges; Bifurcation, is the

point on the ridge from which two branches deriving [9].

Local Binary Pattern

The Local Binary Pattern technique assigns label to every pixel in an image by

means of thresholding. This is achieved by using a 3 × 3 neighborhood system

with the help of equation 1. 7

0( , ) ( )2

p p

m m p mpLBP X Y s g g

(1)

where, pg is the intensity of central pixel, gm is neighborhood pixel intensity, p

indicates number of pixels in neighborhood on circle of radius R . The sign

function ( )s x is given by,

1( )

0

forx as x

forx a

(2)

Fingerprints consist of micro patterns that can be better described by LBP

operator. It is highly discriminative and has less computational complexity.

Figure 4: LBP Feature Extracted Histogram of Live Fingerprint


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Figure 4 shows the histogram of LBP feature extracted from a live fingerprint.

The matching of image pair is done by calculating the distance between two

LBP feature histograms.

Lesser the distance between histograms indicates more similarity in images. The

algorithm for matching a partial and full image pair is based on distance

between two LBP feature histograms [10]. Table V illustrates Fingerprint

accuracy parameters such as FAR, FRR and TSR values with respect to

threshold value, for Fingerprint samples acquired from our database.

Table V: Finger Print FAR/FRR/TSR Values


1.0e+03 0 74.0385 25.9615

2.5e+03 2.8846 67.3077 32.6923

5.0e+03 18.2692 33.6538 66.3462

7.0e+03 26.9231 24.0385 75.9615

9.0e+03 35.5769 15.3846 84.6154

1.0e+04 38.4615 12.5000 87.5000

3.0e+04 49.0385 1.9231 98.0769

Table 6 shows the performance results of Fingerprint algorithm. It depicts the

values of performance parameters for the acquired Fingerprint database. The

FAR and FRR values for Fingerprint biometric authentication are better in real

time database.

Accuracy, sensitivity and specificity are also good. Hence this method is

applicable for real time biometric recognition systems. Figure 5 depicts the

graphical representation of FAR and FRR values for acquired Fingerprint

database, it indicates the threshold values against to error.

Figure 5: FAR/FRR for Fingerprint Database


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Table VI: Finger Print Parameters

1 Accuracy 73.076








Face

Common and natural way of identifying a person is by face recognition. It

distinguishes between two persons. Several features that can be used for

recognition involves nose, lips, eyes etc. It is a non-invasive process where

prominent portion of individual’s face is considered and is converted to its

digital equivalent. A better image source like a good resolution camera and

scanner is used for better accuracy. Most of the facial recognition systems are

designed to work with gray-scale images. Figure 6 shows the facial image

representation of different individual [11].

Figure 6: Individual Facial Images

The recognition problems finally depend on the representation of template. A

unique and simple template set provides better identification and verification

process. In biometric based individual authentication system, physical and

behavioral characteristics like voice, signature, iris and fingerprint recognition

etc are used. But, a main challenge is to make the system safer by avoiding

spoof attacks. It is essential to ensure the vitality detection from the biometric

sample to be used in order to protect the system from spoof attacks. A good

biometric is characterized by use of highly unique features.

It reduces the chance of two persons having similar characteristics and also

prevents the misinterpretation of feature. [12]. The recognition problems, either

verification or identification, depends on the representation of templates. One-

to-one verification or one-to-many identification cases will be easy and straight

forward iff templates remain unique and simple. An actual measurement of the

biometric sample collected from a legitimate and live individual improves the

reliability of a system because it enables the system to reluctance against

artifacts to be enrolled. In most of biometric related authentication system, it is


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difficult to ensure the vitality signs they possess, instead these identifiers are not

confidential. Using ECG as standalone verification for biometric purpose may

not provide sufficient accuracy; instead, a combination of ECG with other sorts

of biometric methods will increase the discriminative information about an

individual. The ECG provides additional information to an unobtrusive

biometric such as fingerprint and face. In this work, it is being shown that, after

combining ECG with fingerprint and face biometrics traits, the performance of

authentication process is enhanced and also increases the robustness against

spoof attacks.

Center-Symmetric Local Binary Pattern

The LBP based face description involves following process: A facial image is

partitioned into local regions and LBP texture descriptors are extracted from

each of these regions separately. The global description of face is obtained by

concatenation of descriptors as shown in figure 7.

The LBP operator produces long histograms and hence it is difficult to make

use of it in the context of a region descriptor. To address this issue, a modified

process of comparison of pixels w.r.t neighborhood is proposed. A center-

symmetric pairs of pixels are considered for comparison and is shown in Figure

7. This reduces the number of comparisons by two for same number of

neighbors. It can be observed that, for eight neighbors, LBP generates 256 (28)

different binary patterns, whereas in CS-LBP it is 16 (24). Further, the

robustness on flat image regions is obtained by thresholding the gray level

differences with a small value T [13].

Figure 7: Face Description with LBP Histogram from Each Block and

Feature Histogram

CS-LBP can be computed by,

12

1( ) ( )2

2

pi

i ii

pCS LBP s g g

(3)

1 1( )

0

ifxs x

otherwise

(4)

where, ig and ( / 2)

ig p are the gray level values of center-symmetric pairs of

pixels with N equally spaced pixels on a circle of radius .R It can be identified

that the CS-LBP is related to gradient operator very closely. This is because

some of the gradient operators consider the intensity differences between the

opposite pixels of neighborhood. In this paper, our consideration is about region

description and there will be no further discussion about operator level

comparison of LBP with CS-LBP [14].


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Table VII illustrates Face accuracy parameters such as FAR, FRR and TSR

values with respect to threshold value, for Face samples acquired from our

database.

Figure 9 depicts the graphical representation of FAR and FRR values for

acquired face database. It indicates the threshold values against to error. The

FAR and FRR values for face recognition based biometric authentication are

better in real time database. Accuracy, sensitivity and specificity are also good.

Hence this method can be used in real time biometric recognition systems.

Table 8 shows the performance results of face algorithm, which depicts all the

values of various performance parameters for the acquired face database.

Table VII: Face FAR/FRR/TSR Values


8.0e+02 17.6471 71.5686 28.4314

1.0e+03 18.6275 70.5882 29.4118

2.5e+03 35.2941 41.1765 58.8235

5.0e+03 48.0392 4.9020 95.0980

7.0e+03 50.0000 0 100.0000

9.0e+03 50.0000 0 100.0000

1.0e+04 50.0000 0 100.0000

Figure 8: FAR/FRR for Face Database

Table VIII: Face Accuracy Parameters

1 Accuracy 76.4706









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3. Multimodal Biometric System

Unimodal biometrics often cannot meet all the system requirements; therefore

combining multiple biometrics can overcome the limitations of unimodal

biometrics and increases the overall performance of the system. In this section

we will discuss the advantages of fusion and explore the different types of

fusion in multi-modal biometric systems. As it is shown in the previous section,

the performance of the ECG biometric system is very sensitive to different

factors which are a challenge for practical applications. Although the proposed

method has a better performance compared to state-of-the-art techniques, still it

is not accurate for many applications. We will specifically discuss the fusion of

ECG with fingerprint, Face and propose a new sequential fusion system. The

fusion of the three biometrics is beneficial and the combined system provides

live detection and performance improvement for both the unimodal systems.

Multimodal biometrics combines information from different sources as opposed

to unimodal biometric system [15]. In multimodal biometric systems fusion is

done at various levels like feature level, decision level and the score level. Each

method of fusion is briefly explained below [16].

Feature Level

Fusion at this level is done by combining more than one feature set extracted

from several data sources that generate a new feature set to represent an

individual as in figure 9. If the set of features taken from one biometric is

independent of another biometric, then it is better to combine two vectors to

form a new single vector, if the features of that biometrics stand under same

kind of measurement scale. Feature Matching: The features obtained from ECG

biometric, Face biometric and Fingerprint biometric are combined for matching

[17]. The trained fused (ECG, Face and Finger) multi biometric features are

compared with the test features using Euclidian distance for authentication. The

fused feature with the minimum Euclidean distance is preferred if it is less than

the threshold value. Tested feature get rejected otherwise. Euclidean distance

can be calculated using the following formula.

( , ) ( )x k kd x w x w (5)

Figure 9: Block Diagram of Feature Level Fusion


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4. Experimental Results

The proposed algorithm is a multimodal biometric recognition system that

involves ECG, Fingerprint and Face. Samples of all the biometric are acquired

and tested for 50 subjects.

Table IX shows the performance results of multi-modal algorithm, which

depicts all the values of various performance parameters for multi-modal

acquired database. The fusion of the acquired database from ECG, Face

Recognition and Fingerprint are achieved. The FAR and FRR values found to

be much better in comparison with those of the unimodal verification. It

indicates that, the fusion process provides better authentication with minimal

error [18].

Table X shows the performance results of unimodal (ECG, Fingerprint and

Face) and multi-modal algorithm, which depicts all the values of various

performance parameters for respective acquired database. It can be observed

that the proposed multimodal system yields better accuracy (73.460) as

compared with unimodal system. Similarly the experimental results of

sensitivity, specificity and other parameters are also good.

Table IX: Multi-Modal Biometric Accuracy

1 Accuracy 73.4602






7 False Discovery Rate(FDR) 26.81723

8 False Negative Rate(FNR) 21.7843

Table X: Performance Results of ECG, FACE, Finger Print and Multi Model

Parameters ECG FACE Finger

Print Multi-model

Accuracy 70.8333 76.4706 73.0769 73.4602

Sensitivity 68.8889 81.4286 69.2308 73.1827

Specificity 74.0741 72.2892 78.4615 74.9416

Positive Predictive

Value(PPV) 81.5789 71.25 81.8182 78.2157

Negative Predictive

Value(NPV) 58.8235 82.1918 64.557 68.5241

False Positive Rate (FPR) 58.8235 27.7108 21.5385 25.0584

False Discovery Rate(FDR) 31.1111 18.5714 30.7692 26.81723

False Negative Rate (FNR) 18.4211 28.75 18.1818 21.7843


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5. Conclusion

The biometric recognition using ECG, fingerprint and face are implemented.

The performance evaluation is recorded on acquired 50 subjects’ database. The

extracted features are stored and corresponding scores are generated, and tests

are performed for various biometric parameters of the captured database. The

recognition rates were computed with respect to FAR and FRR values which are

calculated separately for all modalities shown previously. The algorithm

evaluates 50 subjects and a variety of match rates have been obtained for

different features in the verification. The testing on acquired fingerprint and

face database is performed. The accuracy of fingerprint and face are

satisfactory. The FAR for ECG are good compared to others. The high value of

FAR and FRR tabulated here for the acquired database is attributed to poor

quality images. However, for a standard database the methods would give better

results. It is observed that compared to unimodal biometric authentication

system, multimodal algorithms yields improved results. Our future focus will be

to develop a real-time multi-modal biometric system for larger database and to

provide security by preserving the privacy of the biometric template.

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