Perfo rmance Evaluation of Multimodal Biometrics SystemBiometric authentication has attracted...
Transcript of 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.
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.
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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
Threshold FAR FRR TSR
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
2 Sensitivity 69.2308
3 Specificity 78.4615
4 Positive Predictive Value(PPV) 81.8182
5 Negative Predictive Value(NPV) 64.557
6 False Positive Rate (FPR) 21.5385
7 False Discovery Rate (FDR) 30.7692
8 False Negative Rate (FNR) 18.1818
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
Threshold FAR FRR TSR
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
2 Sensitivity 81.4286
3 Specificity 72.2892
4 Positive Predictive Value(PPV) 71.25
5 Negative Predictive Value(NPV) 82.1918
6 False Positive Rate (FPR) 27.7108
7 False Discovery Rate (FDR) 18.5714
8 False Negative Rate (FNR) 28.75
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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
2 Sensitivity 73.1827
3 Specificity 74.9416
4 Positive Predictive Value(PPV) 78.2157
5 Negative Predictive Value(NPV) 68.5241
6 False Positive Rate (FPR) 25.0584
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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