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International Journal of Advanced Computer Research, Vol 10(47) ISSN (Print): 2249-7277 ISSN (Online): 2277-7970
http://dx.doi.org/10.19101/IJACR.2019.940152
96
A survey of biometric approaches of authentication
Nuhu Yusuf * , Kamalu Abdullahi Marafa, Kamila Ladan Shehu, Hussaini Mamman and Mustapha
Maidawa Lecturer, Department of Management and Information Technology, Abubakar Tafawa Balewa University (ATBU)
Bauchi, Nigeria
Received: 20-December-2019; Revised: 19-March-2020; Accepted: 22-March-2020
©2020 Nuhu Yusuf et al. This is an open access article distributed under the Creative Commons Attribution (CC BY) License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1.Introduction Information security refers to a means of preventing
unauthorized users from access to information. Risk
management usually adopts in information security to
provide solutions to security challenges by
minimizing risks. Risk can be minimized by
administrative control and defense mechanisms.
Access control can also enforce the user right access
and thereby minimizing risks. Authentication is one
of the techniques used in access control systems to
protect unauthorized access. Many approaches for
authentication have been proposed to address security
challenges. These approaches have a conflict with the
system usability and therefore required the
modification of tradition password techniques for
better solutions. Information Authentication is the
common method used by security experts to verify
the users’ identities before getting access right into
the system. Access controls are enforced for all users,
irrespective of categories they belong. Traditional
authentication methods [1] were enough to protect
the unauthorized access right as many security
breaches were reported. Therefore, advanced security
methods that are based on human features required.
*Author for correspondence
Biometrics are a strong authentication method based
on certain human characteristics. These human
characteristics are distinct to each individual and the
selection of each requires careful assessment of its
benefits and shortcomings. Different biometric
methods exist, ranging from simple passwords,
fingerprint and palm print and to more complex ones
such as DNA. The fingerprint is one of the biometrics
methods that are impossible for unauthorized users to
alter because it utilizes friction ridges of the finger.
Palm prints required an image of the hand, palm
region to compare palms for giving access right. As
the most common method, people prepared using a
password to secure their system rather than using
complex algorithms.
Biometric methods prove their capability of
preventing unauthorized user access. However, large-
scale review required on some of the methods to be
able to understand recently added contributions. The
previous contribution on this is given by Padma and
Srinivasan [2] which focused on the biometric
authentication review in cloud computing. Prasad et
al. [3] present fingerprint biometric authentication
methods where they review various fingerprint
recognition system and their application. However,
Review Article
Abstract The increasing need for better authentication methods against hackers has called for the use of the biometric
authentication method to guard against unauthorized access into the systems. The used of human characteristics for
biometrics provides authentication for different kind of systems. However, poor quality of authentication still allows
hackers gaining access to these systems. Many biometrics authentication approaches have been proposed to improve the
authentication accuracy and other related quality measures. This survey aims to provide a state-of-the-art fingerprint and
password biometric authentication approaches. Their challenges have been presented and discussed in terms of biometric
authentication. Furthermore, the strengths and weaknesses of each of the fingerprint and password biometric
authentication are discussed and compared. The findings show that fingerprint image quality and password
authentication is still an active research area where performance requires improvement. Also, the graphical password
indicates a promising future direction for enhancing password methods.
Keywords Biometrics, Authentication, Fingerprint, Password, Information security.
International Journal of Advanced Computer Research, Vol 10(47)
97
there is needed to look into other biometric methods
as the paper only considers the fingerprint method.
This paper presents a survey of biometric
authentication methods, specifically compared
fingerprint and password methods as the most
commonly used by people.
2.Literature review Biometric authentication attracts the attention of both
researchers and practitioners and it’s now replacing
other authentication methods such as passwords. This
is because user behaviour patterns can be easily used
for identification. These human characteristics cannot
be easily stolen or forgot and can useful for
authentications. For instance, face, fingerprint, iris
and voice could easily identify users during
authentication and unauthorized users would not get
access. Tekade and Shende [4] believe that biometric
technology is capable of solving personal identity
security issues for many critical application areas.
Parkavi et al. [5] present the importance of biometrics
in using multiple personal identification techniques
for authenticating users. Kakkad et al. [6] present the
importance of user authentication techniques to
authenticate images on clouds.
The biometric authentication was introduced to
identified and control access to a system [4].
Biometrics can be human characteristics, for
instance, fingerprint, face recognition, iris
recognition, retina and palm print [5]. Through
biometric recognition, users’ identities are verified
based on some certain measurements. To provide
proper authentication, the biometric authentication
usually utilizes fingerprint, eye scanners, facial
recognition, hand geometry and passwords
authentication approaches. The fingerprint approach
operates based on fingerprint scanners such as
optical, capacitive and ultrasound [7]. The optical
takes the finger photo, identify patterns and compile
into codes for proper security identification. Eye
scanner approach provides authentication based on
retina and iris scanner. The retina and iris remain
with a person throughout their life and as such can be
easily accessible. The retina scan uses light to
illuminate eye blood vessels. The idea for this is that
people have different retina tissues in blood vessels.
Iris scanner uses a photo of individuals and uses for
authentication. Facial recognition approach can be
either extracting person face image or using skin
texture analysis for authentication. Hand geometry
approach uses palm thickness for biometric
authentication. Though, the low accuracy [6] serves
as the drawback to this approach. Biometric
authentication provides authentication security
process to verify user identity [4]. The biometric
authentication has been characterized as ease of use
method. This is because users can use it at any time
they required to use. The biometric authentication
also makes difficult for hackers to discover any
weakness and have access to the system [8].
However, a certain limitation exists for biometric
authentication such used by proxy and remote
recovery. To use biometric authentication, the actual
person involve d must be physically present and other
people cannot authenticate on behalf of others.
Additionally, some of the authentication methods do
have recovery methods but there is absent of such
recovery for biometric authentication. Figure 1
presents some of the common biometric
authentication methods.
Figure 1 Biometric authentication methods
Biometric Authentication
Fingerprint
Optical Scanner
Capacitive Scanner
Ultrasound Scanner
Eye Scan
Retina Scan
Iris Scan
Facial Recognition
Face Recognition
Skin Texture Analysis
Hand Geometry
Palm Thickness
Finger Length
Password
One Time Password
Graphical Password
Nuhu Yusuf et al.
98
3.Methods The most often used biometric authentication
approaches in either simple or complex systems are
fingerprint and passwords methods.
3.1Fingerprint biometric authentication
The fingerprint is an important mechanism for
detecting crime and prevents unauthorized access to
the system. Erika Rahmawati et al. [9] believe
fingerprint technology can be used with a digital
signature to improve the security of mobile
applications, specifically when sending and receiving
documents. Furthermore, Kamelia et al. [10] examine
the significant of fingerprint method in taking online
attendance using mobile phones. They provide the
possibility of integrating fingerprint with GPS via
Arduino and achieved 1.39 seconds average response
time. Goicoechea-Telleria et al. [11] investigate how
fingerprint adoption in smartphones becomes a worry
some due to sensor issues. Hwang et al. [12] provide
a template for achieving higher accuracy in
fingerprint recognition for mobile devices. You and
Wang [13] proposed a fingerprint method that is
based on a fuzzy vault scheme. Wireless devices
require fingerprint for data security as such, Lin et al.
[14] suggest dimensional reduction that utilizes
machine learning algorithms as an authentication
solution. Dimensional reductions provide effective
decisions on data reduction. In addition to that, Ma et
al.[15] presents a multi-dimension algorithm to
provide cellular network security. However,
Sadhukhan et al. [16] analyses the performance of
clustering based fingerprint for smartphones devices.
Engelsma et al. [17] suggested how fingerprint can
be enhancing in future to avoid image variation
results from fingerprint captures. They presented a
universal 3d fingerprint target as an alternative to
improve images variations. Similarly, fingerprint
higher resolution in terms of 3d can also be achieved
using sweat gland extraction [18] which utilizes cells
positions. However, Valdes-Ramirez et al. [19]
reviewed fingerprint features for identifying latent
fingerprint based on minutiae. Makhija et al. [20]
analysed the performance of various latent fingerprint
techniques which required further improvements.
3.2Password biometric authentication
Password authentication is the process of verifying
the access right of the user through the use of a
password. User may be allowed to set up a simple
password using text. But these simple texts are
subjects to attacks. Maqbali and Mitchell [21]
suggested the generating of password automatically
without users involvements. This will be in line with
international standard practise for password
requirements authentication.
The purpose of password authentication is to make
authorize users to kept secret access right so that
unauthorized would not get access to. The passwords
should not be easy for password attacks to guess.
Password attackers can easily gain access to weak
passwords. Rahiemy et al. [22] present that the lack
of password complexity serves as the source for
attackers. In addition to that, Tabrez and Sai [23] also
believe that weak passwords always motivate
attackers. Zhang et al. [24] argued that a technique
can design in such a way that user may constantly
change the password before attackers have access.
The technique only takes into consideration the
dictionary attacks while forgetting that other attacks
may provide serious damages than dictionary attacks.
Password attacks are various techniques used to gain
access to the password by either guessing or stealing.
It could be dictionary attacks where people’s names,
date of births, or lower/uppercase letters would be
trying and retry till getting the actual password.
Figure 1 presents how dictionary attacks work.
Erdem and Sandıkkaya [25] support the use of the
one-time password and they proposed a technique
based on OTP where cloud provider would be located
as the cloud as service and then analyze the user
before given access. Default password may be
discovered by either Trojan horse or backdoors via
network trafficking. Intruders also used social
engineering to have access to the passwords via
emails or any other alternative methods. Bruteforce
attacks are other attacks based on trial and error to
get access to the password.
Mohamedali and Fadlalla [26] present different
categories of password attacks and stated the benefits
and shortcomings of each attack. They suggest more
friendly methods to address these attacks without
complicating with usability. These attacks include
among others the Phishing, Man-in-the-Middle, etc.
Zheng and Jia [27] suggest the use of separators
between keystrokes to address the leaked password
issues. This means that the blank space is inserted
within the password for better authentications. If the
passport with spaces corresponds with the users’
inputs, then access right will be granted. However,
Hwang et al. [28] proposed the use of Smart Card as
an authentication method instead of a general
password. They try to address password guessing
attacks using complex smart card implementations.
International Journal of Advanced Computer Research, Vol 10(47)
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4.Results This section presents the results of the two major
biometric authentications taking into consideration
their strengths and limitations.
4.1Results of fingerprint biometric authentication
approach
Table 1 present the comparison of various fingerprint
techniques. Wu and Chiu [29] present solutions to
poor fingerprint quality to ensure better fingerprint
recognition for authentication. Their work used ridge
features techniques which different individuals and
achieved almost 99% accuracy. In addition to that,
Tang et al. [30] examine how Hessian matrix and
short-time Fourier transform (STFT) would improve
fingerprint images quality utilizes fingerprint
textures. The result indicates 0.799 second processing
time has been reduced. Furthermore, Liban and Hilles
[31] suggest enhancing latent fingerprint to improve
fingerprint quality so that reasonable processing time
would be achieved. However, Koptyra and Ogiela
[32] argued that higher fingerprint processing time
will be achieved if enhancing Histograms of Oriented
Gradients (HOG) technique.
Patel et al. [33] enhanced O’ Gorman filter to address
minutiae points’ extraction problem. The result
achieved mean square error (MSE) and peak signal to
noise ratio (PSNR) of 6% and 39% respectively.
Similarly, Sudiro et al. [34] used Artificial Neural
Network to address Fingerprint extraction issues
while achieving 41% False Acceptance Rate. Kim et
al. [35] also used a deep neural network to address
issues arising from fingerprint collections. Cao and
Jain [36] present fingerprint synthesis technique to
reduce processing time error of fetching fingerprint
images from the database. Nuraisha and Shidik [37]
stated that fake fingerprints cause longer processing
time and as such normalization is required to get
higher accuracy results. Han et al. [38] improve
fingerprint image impulse noise using Adaptive
Median Filter.
4.2Results of password biometric authentication
approach
With all the security challenges of traditional
password, Taufiq and Ogi [39] suggest improvement
of existing passwords techniques to strengthen
security rather than adopting other complex methods.
They present a method that utilizes one-time
password known as Raspberry Pi at the access
control level. Though it is difficult for attackers to
repay attacks on password because the new password
will be assigned, the network response time will force
another challenge. Furthermore, Zaki et al. [40]
believe that text passwords can be enhanced using
different pattern keys ranging from simple to
complex ones. However, Lekshmi et al. [41]
suggested the neural network approach as an
alternative password method, especially if integrated
with fuzzy rules. Bhola et al [42] examine how
android device will be used to improve on password
methods. Scaria and Megalingam [43] present a
complex method that incorporates OTP, biometrics
and noisy passwords.
Graphical passwords have been successfully
implemented to overcome text-based passwords
challenges but still required more improvements.
Bilgi and Tugrul [44] integrated images in a
password method to provide access right. Their
approach provides more benefits compared to
ordinary text-based passwords but does not clearly
state how shoulder surfing attacks would be
minimized. Moreover, Fayyadh et al. [45] present a
graphical method that allows the user to create shapes
during their registration and thereby required to draw
such shapes when accessing the system. Their
approach is quite an improvement compared to Bilgi
and Tugrul [44]. Zhang et al [46] approach is difficult to implement and can be conflicting with
usability. Table 2 shows the evaluation of password
biometric authentication approaches.
Table 1 Evaluation of Fingerprint Biometric Authentication Approaches
Authors Problem Techniques Metrics/results Benefits Limitations
Wu and
Chiu (2017) [29]
poor
fingerprint
quality
Ridge Features Accuracy of
99.00%, and
99.09%
Successfully classified
ridges features
Not suitable for large
datasets
Patel et al.
(2017) [33]
minutiae
points
extraction
Enhanced
O’Gorman Filter
MSE = 6.698
PSNR = 39.871
Better results on
O’Gorman compare to
Gabor
Doesn’t show overall
fingerprint
performance
Cao and Jain
(2018) [36]
fingerprint
images
database
Fingerprint
Synthesis
Time: 512 × 512
in 12 muinute
Provide a better quality
image
Doesn’t incorporating
diversity criteria in
the training process
Nuhu Yusuf et al.
100
Authors Problem Techniques Metrics/results Benefits Limitations
Abdilahi
Liban and
Hilles
(2018) [47]
Fingerprint
images
quality
Enhanced Latent
fingerprint
RMSE =
0.023199
PSNR = 81.07826
improved matching
accuracy
latent fingerprint
images still
overlapped
Safira
Nuraisha et
al (2018)
[37]
fake
fingerprints
Normalization Accuracy of 24% Increased accuracy of
detecting fake
fingerprint images
Inefficient features
extraction
Szymkowski
and Saeed
(2018) [48]
Fingerprint
recognition
Sectorization Accuracy of
100%
Provide a new way to
reach a satisfactory
level of identification
accuracy
Changes in changes in
their fingerprint
patterns may still
present
Han et al.
(2018) [38]
filtering
window
size noise
Adaptive Median
Filter
PSNR = 44 Present feasible for
fingerprint image
enhancement
impulse noise still
present
Kim et al.
(2019) [35]
Collecting
fingerprints
deep neural
networks
average detection
error rate=1.57%
Generate real
fingerprint with certain
characteristics
Time-consuming
Sudiro et al.
(2017) [34]
Fingerprint
extraction
simple minutiae
point extraction
FAR= 41.57%
FRR= 41.13%,
EER= 41.35%
minutiae extraction
improvement
still lack accuracy due
to the high value of
FAR
Tang et al.
(2017) [30] Fingerprint
Image
quality
Hessian matrix and
short-time Fourier
transform (STFT)
Processing time =
0.799 s
increased the contrast
greatly according to the
structural characteristics
low contrast between
ridge still need
improvement
Table 2 Evaluation of password biometric authentication approaches
Authors Problem Techniques Benefits Limitations
Taufiq and Ogi
(2018) [39]
password
leakage
attacks
Raspberry Pi Improve one-time password
mutual
authentication
Run with RSA
Zaki et al.
(2018) [40]
password
authentication
combination of pattern,
key, and dummy digits
minimizes different password
attacks and usability issues
Expensive to implement
Lekshmi et al.
(2018) [41]
password
authentication
Hopfield Neural Network
with fuzzy logic
provides better accuracy and
response time
Not easier compare to
graphical passwords
Bhola et al.
(2017) [42]
Cybercrimes Android Device and One-
Way Function
Improved dynamic password
Authentication
Cannot handle multiple
websites authentication
Bilgi and Tugrul
(2018) [44]
password
authentication
Shoulder-Surfing
Resistant Graphical
passwords
faster and easier
authentication processes
shoulder surfing problem
Mehrube and
Nguyen (2018)
[49]
password
authentication
Real-time Eye Tracking The smart camera can capture
and store PIN
incorporating the PIN
identification algorithm into
the-real-time
Othman et al.
(2018) [50]
password
authentication
Graphical Authentication
with Shoulder Surfing
Resistant
demonstrate the robustness,
security strength and the
functionality
A higher number of
direction authentication
exposure
Fayyadh et al
(2018) [45]
password
authentication
graphical password (2D
Shapes)
effective against the brute
force attacks, the dictionary
attacks, and the keylogger
attacks
Difficult to remember the
number of used shapes
when larger
Sudramurthy et
al (2017) [51]
password
authentication
Honey Password Pointed out the strength of the
honey word system depends
on the AES Algorithm
Limited to online purchase
5.Discussion In section 3, the biometric authentication methods are
presented to provide proper authentication. The
fingerprint authentication accuracy and PSNR have
been observed with different levels of performance.
Reasonable results have been obtained for accuracy
which indicates how accurate some of these
techniques have in addressing fingerprint challenges.
The PSNR results indicate additional improvement is
required. Moreover, the techniques used are mostly
International Journal of Advanced Computer Research, Vol 10(47)
101
enhancement of the existing techniques for
fingerprint and their limitations was highlighted.
Additionally, the techniques appear to solve certain
problems, especially for poor quality recognition. For
instance, ridges feature technique successfully
classified and improved poor quality of the
fingerprint. Despite the quality of this technique in
recognizing fingerprint, the ridges feature technique
not suitable for large datasets. This is because of
difficulties in counting the number of fingerprint
ridges. O’Gorman filter technique has also limitation
in showing fingerprint performance when compared
to other methods such as Gabor in extracting
minutiae points. The fingerprint synthesis doesn’t
take diversity into account when addressing the
fingerprint image database. The latent fingerprint and
sectorization techniques improved accuracy, but the
image still overlapped while minimization contains
low accuracy results. This is because fake fingerprint
may be difficult to detect if complex mechanisms
have not put in place for detection. A deep neural
network is time-consuming in collecting fingerprints.
The STFT technique also provides efficient, timely
results due to contrast increased. Moreover, impulse
noise is still present in the adaptive media filter
technique. In password, biometric authentication
methods, graphical password techniques have some
issues regarding the remembering of a various
number of shapes which may lead to poor
authentication. Though, the graphical passwords
techniques provide an effective measure against
hackers. Using Hopfield neural network with fuzzy
logic can possibly eliminate this problem. The
Hopfield neural network with fuzzy logic can provide
better accuracy for authentication compared with
some of the graphical password’s techniques. Pattern
key and dummy digits are expensive to implement
compared with Raspberry Pi based on the one-time
password. Real-time eye-tracking techniques can be a
good technique for authentication compared with
smart camera capture with store PIN which is easily
altered.
6.Conclusion and future work Biometric authentication is identification and
verification, which consider human characteristics to
improve system security. The aim is to identify and
authenticate access to any component of the system.
There are many biometric authentication methods
currently available. This work only considers the two
most widely used methods which are the fingerprint
and passwords methods. Various proposed
fingerprint techniques show much improvement in
achieving high image quality. However, the
fingerprint image quality still required improvement
to recognize fingerprint. Moreover, the password
method comprises text and graphical passwords.
Graphical password authenticates users based on the
grid selection algorithm. The algorithm can prevent
not only shoulder surfing attacks, but also other
related password attacks. Besides that, we
highlighted some features of various biometric
authentication techniques. Additionally, we discussed
some of the strengths and challenges of biometric
authentication. In general, both fingerprint and
password methods have proved effective for
biometric authentication. However, regarding future
work, all simple and complex biometric
authentication methods should be considered for a
better understanding.
Acknowledgment We wish to thank the Department of Management &
Information Technology ATBU Bauchi, Faculty of
Management Science ATBU Bauchi as well as the
Management of Abubakar Tafawa Balewa University
Bauchi for their support and encouragement.
Conflicts of interest The authors have no conflicts of interest to declare.
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Nuhu Yusuf is a Lecturer in the
Management and Information
Technology Department at Abubakar
Tafawa Balewa University (ATBU)
Bauchi, Nigeria. He is currently
pursuing his Ph.D. in Information
Technology. His current research areas
are Data Science, Big data, Data
mining, Information Security, Human Computer Interaction
and Artificial Intelligence.
Email: [email protected]
Kamalu Abdullahi Marafa is a
Lecturer in the Management and
Information Technology Department at
Abubakar Tafawa Balewa University
(ATBU) Bauchi, Nigeria. He received
his Master of Science in Management
Information Technology in 2020. His
current research areas are Cloud
Computing, Information System, Information Security and
Human Computer Interaction.
Email: [email protected]
Kamila Ladan Shehu is a Lecturer in
the Management and Information
Technology Department at Abubakar
Tafawa Balewa University (ATBU)
Bauchi, Nigeria. She is currently
pursuing her Ph.D. in Management
Information Technology. Her current
research areas are Electronic Business,
Technology Management, Information Security and Human
Computer Interaction.
Email: [email protected]
Nuhu Yusuf et al.
104
Hussaini Mamman is a Lecturer in the
Management and Information
Technology Department at Abubakar
Tafawa Balewa University (ATBU)
Bauchi, Nigeria. He received his
Master of Science in Management
Information Technology 2019. His
current research areas are Data Science,
Big data, Data mining, Information Security, Cloud
Computing and Artificial Intelligence.
Email: [email protected]
Mustapha Maidawa is a Lecturer in
the Management and Information
Technology Department at Abubakar
Tafawa Balewa University (ATBU)
Bauchi, Nigeria. He is currently
pursuing his Ph.D. in Management
Information Technology. His current
research areas are Cloud Computing,
Database Management and Information Security.
Email: [email protected]
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