HISTOGRAM OF NEIGHBORHOOD TRIPARTITE AUTHENTICATION WITH FINGERPRINT-BASED BIOMETRICS FOR IOT SERVICES

International Journal of Computer Networks & Communications (IJCNC) Vol.11, No.5, September 2019
DOI: 10.5121/ijcnc.2019.11502 21
HISTOGRAM OF NEIGHBORHOOD TRIPARTITE
AUTHENTICATION WITH FINGERPRINT-BASED
BIOMETRICS FOR IOT SERVICES
S. Kanchana
Department of Computer Science, PSG College of Arts & Science, Coimbatore, India
ABSTRACT
Internet of Things (IoT) and services is an interesting topic with a wide range of potential applications like
smart home systems, health care, telemedicine, and intelligent transportation. Traditionally, key agreement
schemes have been evaluated to access IoT services which are highly susceptible to security. Recently,
Biometric-based authentication is also used to access IoT services and devices. They are involving a larger
amount of memory with increased running time and found to be computationally infeasible. To provide
robust authentication for IoT services, Histogram of Neighborhood Tripartite Authentication with
Fingerprint Biometrics (HNTA-FB) for IoT services is proposed in this paper. This proposed HNTA-FB
method uses binary patterns and a histogram of features to extract the region of interest. To reduce the
memory requirements while providing access to IoT services, Histogram of Neighborhood Binary Pattern
Pre-processing (HNBPP) model is proposed. The discriminative power of Neighbourhood Binary Pattern
Registration (NBPR) is integrated with the normalized sparse representation based on the histogram.
Additionally, this work presents a new Tripartite User Authentication model for fingerprint biometric
template matching process. When compared with different state-of-the-art methods, the proposed method
depicts significantly improved performance in terms of matching accuracy, computational overhead and
execution speed and is highly effective in delivering smart home services.
KEYWORDS
Binary Patterns, Fingerprint Biometrics, Histogram, Internet of Things, Neighborhood Tripartite
Authentication.
1. INTRODUCTION
The emergence of Internet-of-Things (IoT) paradigm permits advanced opportunities for medical
devices with wireless connectivity, by facilitating monitoring of data and also the management of
data in a un-interruptive manner. Munish Bhatia et al. investigated an intelligent healthcare
framework based on IoT to ensure ubiquitous healthcare to the patient during his/her workout
sessions [1]. Real-time health-oriented and non-health oriented attributes are acquired using
several IoT devices, in the data accumulating layer. In the data categorization layer, the attributes
have been acquired using several IoT devices were transmitted wirelessly to cloud storage.

22
Next, the data abstraction layer performed mining using temporal mining using the attributes and
further quantified it in the form of a probabilistic parameter, called as Probabilistic State of
Vulnerability (PSoV).
Finally, PSoV was utilized to train using back propagation ANN to ensure healthcare services in a
un-interruptive manner during exercise sessions. With this, the accuracy of IoT-assisted smart
workouts was said to be improved with high rate of statistical measurements for efficiency and
accuracy. However, the running time consumed to facilitate IoT-assisted smart workouts was not
concentrated. One of the fields that have experienced notable research attention for user
authentication in IoT devices and services is by biometric features. Biometric identifiers depend
on either physiological or behavioral patterns. This facilitates access through the smart device by
the user from anywhere, anytime and anyplace which causes security-critical to IoT. A
lightweight Biometric-based remote user authentication and key agreement scheme, called Multi-
factor biometric user authentication was proposed for secure access to IoT services [2].
The security aspects were proved to be robust against multiple security attacks. Four different
phases such as user registration, login, authentication and password change were used. Multi-
factor biometric user authentication used only one-way hash, perceptual hash, and XOR
operations and found less expensive [3]. Though security was said to be improved, the memory
requirements to perform multi-factor biometric user authentication for IoT services was not
concentrated. A vision-based hand gesture recognition model was investigated for the Internet of
Things to provide a high-security system [4]. The design included edge segmentation and gesture
estimation modules. With these two modules, security was said to be ensured. However,
measures were not taken to safeguard against prominent attacks.
Biometric-based authentication and key agreement scheme were investigated it not only reduced
the communication overhead but also minimized the computation cost involved [5]. However,
existing security mechanisms, such as vision-based recognition model based on biometric along
with gesture recognition model may not be efficient and reliable for providing secure
communication solutions between devices in IoT. To this end, an end-to-end secure IoT-based
solution was investigated with the aid of biometrics and pairing-based cryptography [6].
As the biometric features like face, fingerprint, iris and so on are unique, a biometric-based
security solution is less susceptible to security breaches for IoT infrastructure. The computational
cost multi-factor authentication model using Burrows-Abadi-Needham logic [7] was found to be
higher. Extreme Learning Machine was applied to extract facial emotions to improve recognition
accuracy [8].
A security layer between IoT and users for smart cities using ECG and fingerprint biometric
authentication was proposed [9]. With these two traits, security aspects and improved equal error
rate were also addressed. However, the accuracy with error rate was remained unaddressed. To
provide solution to this issue, ECG-based Continuous Authentication system was designed [10].
To provide security in cloud-based IoT, quantum resistance signature schemes were applied [11].
However, the computational cost and memory was found to be compromised with increasing
features.

23
To resolve these aforementioned problems, a fingerprint-based biometric authentication model
for IoT-based services is presented to ensure the confidentiality of sensitive IoT devices. IoT
gateway node tries to authenticate the user with the fingerprint biometric received from the user
and the hand fingerprint template saved during the registration phase. Finally, user and IoT
gateway node was made to achieve the mutual authentication. Our method achieves stronger
security and shows improved performance on the communication overhead with minimum
running time. The remaining part of this paper is organized as follows. Section 2 reviews the
biometric authentication methods to access IoT services. Section 3 describes the proposed
HNTA-FB method in detail. Section 4 provides the performance evaluation of the proposed
methods compared with the state-of-the-art methods. Finally, Section 6 presents the conclusion.
2. RELATED WORKS
Research efforts have been dedicated to the construction of more precise, usable and secure
biometric authentication mechanisms. Feature de-correlation algorithm was designed for
analyzing security aspects along with mechanisms to combat against several attacks [12]. Jun Xu
et al. proposed hand gesture recognition method using pictorial structure and edge information for
ensuring accuracy under complex backgrounds, but interoperability rate and flexibility was said
to be compromised. To resolve these issues, semantic-based IoT information services was
designed to address security aspects and scalability for open IoT service platform [13].
To provide flexibility, a Network Function Virtualization (NFV) method was presented based on
the Representational State Transfer (REST) architecture [14]. When considering communication
between IoT devices, security is one of the major aspects to be addressed. Multi-layer parameters
were introduced by Paul Loh Ruen Chze and Kan Siew Leong lessens resources with minimum
routing information [15]. As a result, secure multi-hop routing was said to be ensured. Similarly,
uniform access mechanism for heterogeneous things was provided using Field Programmable
Gate Array (FPGA) and System on Chip (SoC)[16].
One of the biggest challenges for IoT access is facing two risks namely, safety threats and privacy
violations. To provide the solution to address these risks, the survey of advanced algorithms using
genetic programming, fuzzy set theory was designed by Jong Hyuk Park and Neil Yuwen Yen
[17]. A comprehensive review of multi-biometric fusion methods for accessing IoT services was
investigated [18]. However, the authentication accuracy was not concentrated. To address these
complications, a novel authentication framework was designed using device-specific information
[19]. In this paper, we present a biometric binary pattern representation and authentication
method for IoT services using the fingerprint features with minimum computational time and
overhead.

24
3. PROPOSED METHODOLOGY FOR BINARY PATTERN REGISTRATION AND
AUTHENTICATION
A Histogram of Neighborhood Tripartite Authentication with Fingerprint Biometrics (HNTA-FB)
for IoT services of human individuals is introduced by extracting the fingerprint features to the
IoT network domain area. The system model description is preceded by elaborate description is
provided in the follow ing sections.
3.1 Proposed framework
Initially, data model of the proposed HNTAFB method using Star IoT Network (SIN) is designed.
The advantage of star IoT network is that all the complexity in the design of the network is
managed by a central node or IoT Gateway Node ‘𝑮𝑵’. The model of SIN involves an IoT
gateway node through which the users are connected to perform several activities. All the other
nodes only need to communicate in their time slot based on Time-Division Multiple Access
(TDMA) basis. Figure 1 shows the system model using SIN.
Figure 1. Star IoT Network
As shown in figure 1, SIN in the proposed method comprises a set of entities representing the set
of users connected with the aid of a relationship set ‘𝑆𝐼𝐺’. The relationship set is mathematically
formulated as given below.
𝑆𝐼𝐺 → (𝑈, 𝑅, 𝐶) (1)
From (1), the star IoT graph ‘𝑆𝐼𝐺’ in the proposed method comprises the set of users ‘𝑈’, with
the relationship set denoted as ‘𝑅𝑆’ and relational coefficient denoted as ‘𝐶’ respectively. Each
user ‘𝑈’ is defined as below.
𝑈6 𝑈5
𝑈2 𝑈3
𝑈1 𝑈4
IoT Gateway Node ‘𝑮𝑵’- Users -

25
𝑈 ∈ 𝑈𝑖 (2)
𝑈𝑖 → 𝑈𝑖 ∪ 𝑓𝑝1, 𝑓𝑝2, … , 𝑓𝑝 𝑛 (3)
From (3), ‘𝑈𝑖’ represent the set of users, where, ‘𝐹𝑃𝑖’ represent a fingerprint attribute of the user
with fingerprint feature sets ‘𝑓𝑝1’, ‘𝑓𝑝2’ and so on extracted at different time settings ‘𝑡1’, ‘𝑡2’
respectively. The relationship set in the method is represented as ‘𝑅𝑆{𝑈, 𝐼}’ in the IoT setting,
where ‘𝑈’ represents the nodes or users and ‘𝐼’ corresponds to the relationship or interactions that
connect between the users and IoT devices. Here, the nodes or users are represented as points
whereas the interactions between the users and IoT devices are presented as lines. Besides, the
coefficient value ‘ 𝐶 ’ symbolize{𝑈𝑖} ∗ 𝑈 → 𝑟, 𝑤ℎ𝑒𝑟𝑒 𝑟 ∈ 𝑅𝑆’ corresponds to a function that
assigns a relationship type ‘𝑟’ between a given user, ‘𝑈𝑖’ and IoT services.
3.2 Problem formulation
Let us consider a network environment of ‘n’ users and ‘𝛾1
, 𝛾2
, … , 𝛾 𝑛
’ templates of the ‘𝑛 𝑢𝑠𝑒𝑟𝑠’
in a biometric system for IoT services. Let us further assume that the ‘n’ users communicate with
the IoT devices via IoT Gateway Node ‘𝐺𝑁’ using fingerprint as a biometric modality to provide
an effective user authentication scheme for IoT services in a safe and controlled environment of
the biometric system. Hence, the nth
user template is of the form ‘𝜑 𝑛
= { 𝛾1
𝑛
}’ consists of single
units where ‘𝛾1
𝑛
’ denote the templates for fingerprint. Our objectives are defined by designing an
IoT smart home network setting. An IoT Gateway Node is introduced along with the fingerprint
biometric features of human individuals to measure the authenticity of the user and access IoT
services.
3.3 Proposed method
In this section, a methodology for an effective security scheme in smart home IoT devices via
fingerprint technique is investigated. Figure 2 represents the proposed method, Neighborhood
Tripartite Authentication with Fingerprint Biometrics (HNTA-FB) for IoT services. HNTA-FB
method is used as an authentication scheme for smart home monitoring by IoT device. To address
security issues in IoT via biometrics using fingerprint as a modality, three steps are followed.
They are user registration or feature extraction, preprocessing and matching or authentication.

26
Figure 2. Block diagram of HNTA-FB for IoT services
As shown in figure 2, the user accesses the IoT devices through IP router, the system authorizes
and validates the user through fingerprint images which are stored as templates. If the user fails to
authorize him/her through fingerprint recognition as stored in templates. This fingerprint module
can be integrated with different types of sensors like automated door lock, different electronic
devices, security devices and so on. In system implementation, a biometric fingerprint security
model is developed for smart home monitoring.
3.4 Neighborhood Binary Pattern Registration (NBPR) model
The proposed HNTA-FB method initially performs user registration with user node, gateway
node ‘ 𝐺𝑁’ and IoT nodes or IoT devices. To register fingerprint features, a unique ID is
generated for each user. Along with the registration between the user node, gateway node and IoT
nodes, the user’s biometric fingerprint features are extracted. It is denoted by ‘𝐼𝐷 𝑢’, for a user
‘𝑢’. Similarly, any number of users registers their fingerprint in the biometric system and obtains
a unique ID. Multiple fingerprint impressions of the same user are obtained and stored. Besides,
the fingerprint feature registration in the proposed method is performed with the help of minutia
features. A minutia for fingerprint impression is symbolized as below.
𝑀 = (𝑃, 𝑄, 𝜃) (4)
From equation (4), the minutia features ‘𝑀’ are obtained for each user ‘𝑈𝑖 ’ both using the
location information ‘(𝑃, 𝑄)’ and direction ‘𝜃’ information respectively. In this work, user
IoT
Gateway
nodes
Home IoT
device
Matching
Fingerprint
templates
Internet
Fingerprint
image
IP Router
Preprocessing
N
Y

27
registration for the biometric fingerprint is performed using Neighborhood Binary Pattern
Registration (NBPR) model. Each neighborhood pixel for the corresponding location information
is represented as a binary pattern and is denoted by two bits. In binary patterns, the two bits is
Most Significant Bit to represent the flag symbol and Least Significant Bit to represent the
magnitude. The pseudo-code representation of the Neighborhood Binary Pattern Registration
(NBPR) is given below.
Input: user node ‘𝑈 = 𝑈1, 𝑈2 , … , 𝑈 𝑛’, gateway node ‘𝐺𝑁’ and IoT nodes
or IoT devices ‘𝐷 = 𝑑1, 𝑑2, … , 𝑑 𝑛’, fingerprint impressions ‘𝐹𝑃𝑖’,
Output: Registered values ‘𝑅𝑉(𝑖, 𝑗)’
1: Begin
2:For each user node ‘ 𝑈’ with gateway node ‘GN’;IoT devices ‘ 𝐷’
fingerprint impressions ‘𝐹𝑃𝑖’
3:Obtain minutia for fingerprint impression using (4)
4: Obtain flag bit patterns using (5)
5: Obtain magnitude bit patterns using (6)
6: End for
7: End
Algorithm 1. Neighborhood Binary Pattern Registration
As given in above, NBPR algorithm gives both the flag ‘𝐹’ and magnitude ‘𝑀’ components for
each pixel from its neighboring pixel. Let us consider ‘𝑚𝑝’ as the midpoint pixel and ‘𝑠’ as the
number of neighbors of a midpoint pixel, then the first ‘𝑠 𝑀𝑆𝐵 𝑏𝑖𝑡𝑠’ represent flag and the next
‘𝑠 𝐿𝑆𝐵 𝑏𝑖𝑡𝑠’ represent magnitude.
The fingerprint image is scanned on its left-top, left-middle, left-bottom and right-top and
considering each pixel which is surrounded by 8 neighboring pixels, forming a ‘3 ∗ 3’ matrix.
The midpoint pixel depth value is ‘𝑑 𝑚𝑝’ and neighboring pixel depth values are referred to as
‘𝑑 𝑛𝑝’. Then, the flag bit patterns for ‘3 ∗ 3 𝑚𝑎𝑡𝑟𝑖𝑐𝑒𝑠’ are generated using the mathematical
formulae as given below.
𝐹(𝑖, 𝑗) = {
0, 𝑑 𝑚𝑝 − 𝑑 𝑛𝑝 ≤ 0
1, 𝑑 𝑚𝑝 − 𝑑 𝑛𝑝 > 0
(5)
From equation (5), the flag bit patterns are generated by first identifying the difference between
the midpoint pixel and the neighboring pixel. If the resultant value is less than zero, the flag bit is
set as ‘0’, otherwise the flag bit is set as ‘1’. The magnitude bit patterns for ‘3 ∗ 3 𝑚𝑎𝑡𝑟𝑖𝑐𝑒𝑠’ are
generated using the mathematical formulae as given below.

28
𝑀(𝑖, 𝑗) = {
0, 𝑑 𝑚𝑝 − 𝑑 𝑛𝑝 ≤ 𝑠
1, 𝑑 𝑚𝑝 − 𝑑 𝑛𝑝 > 𝑠
(6)
From equation (6), the magnitude bit patterns are generated by first identifying the difference
between the midpoint pixel and the neighboring pixel. With the obtained resultant value, the
actual value is compared with the resultant value. If the resultant value is less than the actual
value, then the magnitude bit pattern is set as ‘0’, otherwise, the magnitude bit pattern is set as
‘1’.The sample representation of NBP is as shown in Figure 3.
Figure 3. Representation of Neighborhood Binary Pattern
The arbitrary values for ‘3 ∗ 3’ matrix are considered in Figure 3(a). The reduced dimensionality
values are shown in Figure 3(b). The flag bit of each coefficient is represented in Figure 3(c). The
magnitude components of NBP are shown in Figure 3(d). Finally, the resultant registered value is
obtained by comparing the actual values and resultant difference values and is represented in
figure 3(e). As a result, small scale appearance of the biometric fingerprint image is obtained.
As both the pixel difference and magnitude difference is used, the model is found to be
computationally simple, therefore utilizing lesser memory. In other words, by applying
Neighborhood Binary Pattern for biometric fingerprint registration for IoT devices, running time
is said to be reduced.
3.5 Histogram of Neighborhood Binary Pattern Preprocessing model
After obtaining NBP values from fingerprint images, preprocessing of fingerprint images is
performed to extract the region of interest using Histogram of Neighborhood Binary Pattern
Preprocessing (HNBPP) model for fingerprint features. Dominant region of interest is stored in a
vector for fingerprint features and form the fingerprint template of the human individuals.
Three key modules are presented in the proposed HNBPP model namely, fingerprint gradient
extraction, gradient direction, and proposed fingerprint normalization. In the HNBPP model, with
the registered values (i.e. fingerprint features, user ID and fingerprint impressions) as input,
preprocessing starts with the fingerprint gradient extraction. Here, vertical extraction and
horizontal extraction are performed for pixel depth ‘𝑑’. It is mathematically formulated as given
below.

29
𝐺𝑖(𝑗, 𝑖) = 𝑑 (𝑗, 𝑖 + 1) − 𝑑 (𝑗, 𝑖 − 1) (7)
𝐺𝑗(𝑗, 𝑖) = 𝑑(𝑗 + 1, 𝑖) − 𝑑 (𝑗 − 1, 𝑖) (8)
From the above equations (7) and (8), ‘𝐺𝑖’ represent the vertical fingerprint extraction and ‘𝐺𝑗’
represents the horizontal fingerprint extraction with ‘d(i,j)’ denoting the pixel depth at
coordinates ‘𝑖’ and ‘𝑗’ respectively. With the resultant fingerprint gradient extraction, next, the
direction of extracted fingerprint gradient is formed. It is mathematically represented as given
below.
𝜃 (𝑗, 𝑖) = 𝑎𝑟𝑐𝑡𝑎𝑛 [
𝐺 𝑗(𝑗,𝑖)
𝐺 𝑖(𝑗,𝑖)
] (9)
Finally, fingerprint normalization is performed using the histogram obtained through vertical
extraction, horizontal extraction, and direction of extracted fingerprint gradient. It is
mathematically formulated as given below.
𝐿1 𝑛𝑜𝑟𝑚 ∶ 𝑓 =
𝑉
|𝑉|+ 𝑐
(10)
The pseudo-code representation of Histogram of Neighborhood Binary Pattern Preprocessing
(HNBPP) for normalizing the registered values obtained through the NBPR algorithm is given
below.
Input: Registered values ‘𝑅𝑉(𝑖, 𝑗)’, user node ‘𝑈 = 𝑈1, 𝑈2 , … , 𝑈 𝑛’, pixel depth
‘𝑑(𝑖, 𝑗)’, gateway node ‘𝐺𝑁’ and IoT nodes or IoT devices ‘𝐷 = 𝑑1, 𝑑2, … , 𝑑 𝑛’,
fingerprint impressions ‘𝐹𝑃𝑖’
Output: Normalized fingerprint ‘𝑓’
1: Begin
2:For each user node ‘𝑈’ with gateway node ‘𝐺𝑁’,IoT devices ‘𝐷’ fingerprint
impressions ‘𝐹𝑃𝑖’
3:For each Registered Values ‘𝑅𝑉(𝑖, 𝑗)’
4: Obtain vertical extraction and horizontal extraction using (7) and (8)
5: Obtain gradient direction using (9)
6:Stored the resultant of (7), (8) and (9) in vector ‘𝑉’
7: Obtain normalized fingerprint using (10)
8: End for
9:End for
10: End
Algorithm 2. Histogram of Neighborhood Binary Pattern Preprocessing (HNBPP)

30
As given in the above HNBPP algorithm, for each user node with gateway node, IoT devices and
fingerprint impressions as input, preprocessing the registered values obtained through the NBPR
algorithm. The running time using NBPR algorithm was found to be less due to binary pattern
registration, the computational complexity was found to be high. To reduce the computational
complexity involved in fingerprint biometric-based authentication for IoT services, histogram
model is used. By obtaining the histogram via vertical and horizontal factors along with gradient
information and finally, normalizing the fingerprint biometric, the computational complexity is
said to be reduced.
3.6 Tripartite Component User Authentication
With preprocessed fingerprint images of users, the process of template matching is performed to
identify the matching accuracy. With the Tripartite Component User Authentication is applied to
the preprocessed fingerprint images, the matching accuracy of test data to the available training
data (fingerprint templates) are first extracted from the benchmark/real dataset. On the other
hand, no access to IoT devices is said to take place upon unsuccessful matching. Therefore,
whenever the user tries to access the IoT devices, the system validates the user through the
fingerprint module we have included.
The pseudo-code representation of Tripartite Component User Authentication is provided below.
Input: user node ‘𝑈 = 𝑈1, 𝑈2 , … , 𝑈 𝑛’, gateway node ‘𝐺𝑁’ and IoT nodes or IoT devices
‘𝐷 = 𝑑1, 𝑑2, … , 𝑑 𝑛’, fingerprint impressions ‘𝐹𝑃𝑖’, Normalized fingerprint ‘𝑓’
Output: Improved matching accuracy
1: Begin
2:For each user node ‘ 𝑈 ’ with gateway node ‘ 𝐺𝑁 ’,IoT devices ‘ 𝐷 ’ fingerprint
impressions ‘𝐹𝑃𝑖’
3: For each Normalized fingerprint ‘𝑓’
4: If ‘𝑓 ∈ 𝐹𝑃𝑖 ’
5: Verification OK
6: Authentication Succeeded
7: Access of IoT devices
8: End if
9: If ‘𝑓 ∉ 𝐹𝑃𝑖 ’
10: Verification not OK
11: Authentication not Succeeded
12: No access of IoT devices
13: End if
14: End for
15: End for
16: End
Algorithm 3. Tripartite Component User Authentication

31
With the successful matching of the test and trained dataset (obtained from fingerprint template),
access of IoT devices is said to take place. On the other hand, if the user fails to authorize him/her
through fingerprint recognition, he/she cannot access the IoT devices.
4. PERFORMANCE EVALUATION
The experimental evaluation is conducted using biometric samples that are extracted from the
BioSecure datasets distributed by Association of BioSecure. The performance results of the
proposed method are obtained using different training and test dataset samples contain data of
both male and female. The training dataset is of the size 240 fingerprint images whereas the test
dataset includes 150 images of user’s fingerprint to access IoT services towards smart home
systems. The experiments were implemented in MATLAB. The proposed method is compared
with two existing methods namely Intelligent healthcare framework proposed by Munish Bhatia
et al. (2017) and Multi-factor biometric user authentication proposed by Parwinder Kaur Dhillon
et al. (2017). Fingerprint biometric user authentication to access smart home IoT services focuses
on the aspects of matching accuracy and authentication to access smart home IoT services. The
proposed method is evaluated in different aspects such as matching accuracy, Execution speed,
and computational overhead.
4.1 Matching accuracy
The main goal of our experiments is to determine the rate of matching accuracy for fingerprint
biometric user authentication using evolutionary algorithms. Matching accuracy is one of the
performance parameters and is determined as the ratio of biometric samples that were matched
correctly to the density of human biometric samples provided as input during experimentation. A
random selection of 150 fingerprint images out of 240 images was made. The matching accuracy
for fingerprint biometric user authentication to access smart home IoT devices of an individual
user is evaluated by the following formula.
𝐴 = ∑
𝐹𝑢𝑠𝑖𝑜𝑛 𝑡𝑒𝑚𝑝𝑙𝑎𝑡𝑒 𝑚𝑎𝑡𝑐ℎ𝑒𝑑
𝑈 𝑠𝑖𝑧𝑒
∗ 100𝑛
𝑠𝑖𝑧𝑒=1 (11)
From equation (11), the accuracy ‘𝐴’ is measured concerning the total number of fingerprint
biometric samples ‘𝑈𝑠𝑖𝑧𝑒 ’ and measured in terms of percentage (%). Figure 4 illustrates the
matching accuracy for smart home IoT services using fingerprint biometric with a density of
biometric samples in the range of 15 to 150. The decision point of ten different fingerprint
biometric samples was selected in a random manner that achieved a substantial improvement in
ratings. The results show better performance of the proposed HNTA-FB method, but it is not
linear due to the presence of noise in fingerprint biometric images. The final values of the graph
plotted in the figure confirm the working hypothesis that the matching accuracy for smart home
IoT services using fingerprint biometric increases with the increase in the density.

32
Figure 4. Matching accuracy using fingerprint biometric
The HNTA-FB method improved the matching accuracy for biometric authentication using the
extensive Tripartite User Authentication algorithm, by 22% compared to Intelligent healthcare
framework proposed by Munish Bhatia et al. (2017) and by 35% compared to Multi-factor
biometric user authentication proposed by Parwinder Kaur Dhillon et al. (2017). This is because
the HNTA-FB method adapted a fingerprint gradient extraction, gradient direction, and
fingerprint normalization to decide upon the factor whether or not the two representations belong
to the same user, resulting in the improvement of matching accuracy. Furthermore based on the
resultant normalized value, to obtain maximum relevance they were converted based on tripartite
values to access the IoT services that in turn improved the matching accuracy.
4.2 Execution speed
The execution speed for pattern registration is the time required to extract the minutiae features
concerning the template size and is as given below. It is the product of template size considered
and the time is taken for minutiae feature extraction.
𝐸𝑆𝑡𝑖 𝑚𝑒 = 𝐹𝑇𝑠𝑖𝑧𝑒 ∗ 𝑇𝑖𝑚𝑒 (𝑚𝑖𝑛𝑢𝑡𝑖𝑎𝑒 𝑒𝑥𝑡𝑟𝑎𝑐𝑡𝑖𝑜𝑛) (12)
Where EStime is the running time for pattern registration and FTsize refers to the template size
considered during each iteration.
0
20
40
60
80
100
15 30 45 60 75 90 105 120 135 150
Matchingrate(%)
Density of fingerprint biometric samples
HNTA-FB
Intelligent healthcare framework
Multi-factor biometric user authentication

33
Figure 5. Measurement of execution speed for HNTA-FB
Experimental results for execution time are obtained for registration and authentication with
fingerprint biometric templates. Higher, the template size, higher the running time for user
registration. With higher template size, the size of individual fingerprint images grows
exponentially, and therefore the running time for increased template size also increased. But from
figure 5, it is comparatively observed that the proposed HNTA-FB method results from the lower
execution time.
By applying the Neighborhood Binary Pattern Registration (NBPR) algorithm in HNTA-FB
method, fingerprint images are extracted based on the minutiae, comparing their corresponding
neighborhood structures via flag bit and magnitude bit patterns. The redundant information is
removed from obtained fingerprint biometric of a user resulting in minimizing the user
registration time. As a result, the time taken for authentication to access the IoT devices is also
said to be reduced. The process is repeated with the template size of 350KB to 3500KB for
conducting experiments. The results confirm that the running time increases, with the increase in
the template size, though betterment achieved using HNTA-FB method.
As shown in figure 5, when compared to two other methods proposed by Munish Bhatia et al.
(2017) and Parwinder Kaur Dhillon et al. (2017), the HNTA-FB method had better changes using
the extensive NBPR algorithm. The NBPR algorithm applied in HNTA-FB method symbolizes
the mean estimation of fingerprint image by scanning left-top, left-middle, left-bottom and right-
top reducing a certain amount of noise present in the fingerprint. This, in turn, reduces the
running time of user registration and therefore the authentication time for accessing IoT devices
by 19% compared to Intelligent healthcare framework and 40% compared to Multi-factor
biometric user authentication.
0
200
400
600
800
1000
1200
350 700 1050 1400 1750 2100 2450 2800 3150 3500
Executionspeed(ms)
Template size (KB)
HNTA-FB

34
4.3 Computational overhead
Finally, the third goal of our experiments is addressed concerning computational overhead which
is a measure of the amount of working storage required to perform fingerprint biometric template
matching (algorithm). In other words, computational overhead measures the memory required to
execute the algorithm at any point.
𝐶𝑂 = 𝑈𝑠𝑖𝑧𝑒 ∗ 𝑀𝑒𝑚 (𝐺[𝑖, 𝑗]) + 𝑀𝑒𝑚 (𝜃 (𝑗, 𝑖)) + 𝑀𝑒𝑚 (𝐿1 𝑛𝑜𝑟𝑚) (13)
From the above equation (13), the computational overhead ‘𝐶𝑂’ is obtained using the density of
human biometric samples ‘𝑈𝑠𝑖𝑧𝑒’ and memory consumed for gradient extraction ‘𝐺[𝑖, 𝑗]’, gradient
direction ‘𝜃 (𝑗, 𝑖)’ and normalization ‘𝐿1 𝑛𝑜𝑟𝑚’. It is measured in terms of KiloBytes (KB). For
all scenarios as shown in the figure, the computational overhead is increasing with fingerprint
biometric samples considered from different users. Ten unique experiments were conducted for
each review size. The sample calculation is provided below. Followed by the calculations
measured for computational overhead using the proposed HNTA-FB method, Intelligent
healthcare framework and Multi-factor biometric user authentication graphical representation are
provided.
Figure 6. Measure of computational overhead
For better perception of the efficacy of the proposed HNTA-FB method, substantial experiments
are conducted and illustrated in Figure 6. It shows the experimental results of computational
overhead versus the density of fingerprint biometric samples considered. The targeting results of
computational overhead using HNTA-FB method is compared with two state-of-the-art methods
intelligent healthcare framework proposed by Munish Bhatia et al. (2017) and Multi-factor
biometric user authentication proposed by Parwinder Kaur Dhillon et al. (2017). We have
incorporated Histogram of Neighborhood Binary Pattern for fingerprint biometric of the human
individuals which are differed from the state of the art methods. Here the dominant factors are
stored via vertical and horizontal factors along with gradient information and finally, normalizing
0
500
1000
1500
2000
2500
3000
3500
15 30 45 60 75 90 105 120 135 150
Computationaloverhead(KB)
Density of fingerprint biometric samples
HNTA-FB

35
the fingerprint biometric that reduces the dimensionality factor. With the resultant feature vectors
obtained from fingerprint features, the resultant value is stored in vector based on the mutual
information of the individual features. This in turn, reduces the computational overhead arising
during access of IoT devices. Therefore the computational complexity for accessing IoT devices
using fingerprint biometric feature is reduced by 15% compared to intelligent healthcare
framework and 33% compared to Multi-factor biometric user authentication respectively.
5. CONCLUSION
Biometric-based authentication for accessing IoT devices and IoT networks is considered as a
complex task due to the intrinsic features. To safeguard these IoT devices from malicious users,
an efficient method is proposed with minimal complexity and time. In this article, we provide a
method called Histogram of Neighborhood Tripartite Authentication with Fingerprint Biometrics
(HNTA-FB) for IoT services. Initially, the fingerprint features are extracted using Neighborhood
Binary Pattern Registration (NBPR) algorithm. With the extracted features, dominant attributes
are stored in a vector form by applying Histogram of Neighborhood Binary Pattern Preprocessing
(HNBPP) algorithm which resulted in the minimization of computational complexity for several
users with the fingerprint as the biometric feature. The evaluation of the template matching is
performed finally using the Tripartite Component User Authentication algorithm to authenticate
the users so that only authenticated users are allowed to access the IoT devices. Through the
experiments using real traces, we observed that HNTA-FB can be employed as a safe
communication method for successful access of smart home IoT devices with reduced running
time and computational complexity than the existing biometric authentication methods. Thus, the
proposed method is suitable for smart home systems and other applications such as health care,
surveillance and so on.
REFERENCES
[1] Munish Bhatia, Sandeep K. Sood, “A comprehensive health assessment framework to facilitate IoT-
assisted smart workouts: A predictive healthcare perspective”, Computers in Industry, Elsevier, 2017.
https://doi.org/10.1016/j.compind.2017.06.009 .
[2] Parwinder Kaur Dhillon, Sheetal Kalra, “A lightweight biometrics based remote user authentication
scheme for IoT services”, Journal of Information Security and Applications, Elsevier, 2017.
https://doi.org/10.1016/j.jisa.2017.01.003
[3] Ortega-Garcia, Javier, “The multi scenario multi environment biosecure multimodal database" IEEE
Transactions on Pattern Analysis and Machine Intelligence, 2010.
https://doi.org/10.1109/tpami.2009.76
[4] Jun Xu, Xiong Zhang, and Meng Zhou, “A High-Security and Smart Interaction System Based on
Hand Gesture Recognition for Internet of Things”, Hindawi, Security and Communication Networks,
2018. https://doi.org/10.1155/2018/4879496

36
[5] Li Yang, Zhiming Zheng, “Cryptanalysis and improvement of a biometrics-based authentication and
key agreement scheme for multi-server environments”, PLOS ONE, 2018,
https://doi.org/10.1371/journal.pone.0194093
[6] M. Shamim Hossain et al., “Toward End-to-End Biometrics-Based Security for IoT Infrastructure”,
IEEE Wireless Communications, 2016. https://doi.org/10.1109/mwc.2016.7721741
[7] Younsung Choi, Youngsook Lee, Jongho Moon, Dongho Won, “Security enhanced multi-factor
biometric authentication scheme using bio-hash function”, PLOS ONE, 2017.
https://doi.org/10.1371/journal.pone.0176250.
[8] Seyedehsamaneh Shojaeilangari, Wei-Yun Yau, Karthik Nandakumar, Li Jun, Eam Khwang Teoh,
“Robust Representation and Recognition of Facial Emotions Using Extreme Sparse Learning”, IEEE
Transactions on Image Processing, 2015. https://doi.org/10.1109/tip.2015.2416634
[9] Juan S. Arteaga-Falconi, Hussein Al Osman, Abdulmotaleb El Saddik, “ECG and Fingerprint
Bimodal Authentication”, Sustainable Cities and Society, Elsevier, 2017.
https://doi.org/10.1016/j.scs.2017.12.023
[10] Pedro Peris-Lopez, Lorena Gonzalez-Manzano, Carmen Camara, Jose Maria de Fuentes, “Effect of
attacker characterization in ECG-based continuous authentication mechanisms for Internet of
Things”, Future Generation Computer Systems, Elsevier, 2017.
https://doi.org/10.1016/j.future.2017.11.037
[11] Haibo Yi, Zhe Nie, “Side-channel security analysis of UOV signature for cloud-based Internet of
Things”, Future Generation Computer Systems, Elsevier, 2018.
https://doi.org/10.1016/j.future.2018.04.083
[12] Wencheng Yang, Jiankun Hu, Song Wang, Qianhong Wu, “Biometrics Based Privacy-Preserving
Authentication and Mobile Template Protection”, Hindawi Wireless Communications and Mobile
Computing, 2018. https://doi.org/10.1155/2018/7107295
[13] Dong-Hwan Park, Hyo-Chan Bang, Cheol Sik Pyo, Soon-Ju Kang, “Semantic Open IoT Service
Platform Technology”, IEEE World Forum on Internet of Things, 2014. https://doi.org/10.1109/wf-
iot.2014.6803125
[14] Igor Miladinovic, Sigrid Schefer-Wenzl, “NFV Enabled IoT Architecture for an Operating Room
Environment”, IEEE 4th World Forum on Internet of Things (WF-IoT), 2018.
https://doi.org/10.1109/wf-iot.2018.8355128
[15] Paul Loh Ruen Chze, Kan Siew Leong, “A Secure Multi-Hop Routing for IoT Communication”,
IEEE World Forum on Internet of Things (WF-IoT), 2014. https://doi.org/10.1109/wf-
iot.2014.6803204
[16] Shulong Wang, Yibin Hou, Fang Gao, Xinrong Ji, “A Novel IoT Access Architecture for Vehicle
Monitoring System”, IEEE 3rd World Forum on Internet of Things , 2016.
https://doi.org/10.1109/wf-iot.2016.7845396

37
[17] Jong Hyuk Park, Neil Yuwen Yen, “Advanced algorithms and applications based on IoT for the smart
Devices”, Journal of Ambient Intelligence and Humanized Computing, 2018.
https://doi.org/10.1007/s12652-018-0715-5
[18] Lavinia, Mihaela, Dinca, Gerhard Petrus Hancke, “The Fall of One, the Rise of Many: A Survey on
Multi-Biometric Fusion Methods”, IEEE Access (Volume: 5), 2017.
https://doi.org/10.1109/access.2017.2694050
[19] Yaman Sharaf-Dabbagh, Walid Saad, “Demo Abstract: Cyber-Physical Fingerprinting for Internet of
Things Authentication”, ACM/IEEE Second International Conference on Internet-of-Things Design
and Implementation (IoTDI), 2017. https://doi.org/10.1145/3054977.3057323

HISTOGRAM OF NEIGHBORHOOD TRIPARTITE AUTHENTICATION WITH FINGERPRINT-BASED BIOMETRICS FOR IOT SERVICES

More Related Content

What's hot (18)

Similar to HISTOGRAM OF NEIGHBORHOOD TRIPARTITE AUTHENTICATION WITH FINGERPRINT-BASED BIOMETRICS FOR IOT SERVICES (20)

More from IJCNCJournal (20)

Recently uploaded (20)

HISTOGRAM OF NEIGHBORHOOD TRIPARTITE AUTHENTICATION WITH FINGERPRINT-BASED BIOMETRICS FOR IOT SERVICES