An in-depth review on Contactless Fingerprint Identification using Deep Learning

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 03 | Mar 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 143
An in-depth review on Contactless Fingerprint Identification using Deep Learning
Aishwariya Nair1* Sayali Pawar 2* Sakshi Kumbhar 3* Mayuri Zade r4*
1
B.Tech student, Computer Science, RMD Sinhgad School of Engineering, Pune, India.,
2 B.Tech student, Computer Science, RMD Sinhgad School of Engineering, Pune, India,
3 B.Tech student, Computer Science, RMD Sinhgad School ofEngineering ,Pune, India,
4 B.Tech student, Computer Science, RMD Sinhgad School of Engineering ,Pune, India.
-----------------------------------------------------------------------***------------------------------------------------------------------------
Abstract
Biometric authentication has been one of the leading
techniques for the verification of an individual in the recent
electronics paradigm. There have been increase in the
number ofSmartphones and other electronic gadgets such as
laptops thatcome equipped with a fingerprint sensor for the
purpose of authentication of the rightful user. There has
been increased instances of utilization of such biometric
techniques as it is one of the most accurate and full proof
solution for the purpose of authenticating a user. But in the
recent years there hasbeen increased skepticism in utilizing
public biometric authentication scanner devices due to
those devices being covered in dirt and or viruses. Therefore
to improve the paradigmof fingerprint identification there is
the need for effective and useful contact-less fingerprint
identification system. This research paper identifies the
current techniques for contactless fingerprint in which are
used for an effective analysis or review that has been
useful in developing are methodology for this topic which
will be elaborated in the future editions of thisarticle.
Key Words: Biometric Verification, Contactless Fin-
gerprint identification, Image processing, Convolutional
Neural Networks.
Introduction
In a contemporary world where innovation is emerging at
a fast rate, safe verification and recognition of individualsis
necessary. Although solutions such as Card information,
OTP, and Security codes available, typically doesn’t fulfill all
compliance requirements and are susceptible to exploitation.
Biometric verification is more resilient and provides more
reliable verification and identification. In biometric
verification, authorization is granted or the user is
authenticated based on their physical traits.
A person may be recognized by key structural and
psychological characteristics, or by a mixture of these two
types of characteristics. Individuality and identity are
comprised of these behavioral characteristics, which
encompass a person’s ideas, behaviors, speech, posture,
sentiments, etc. In contrast, individuals are conceived with
physical characteristics such as handprint, DNA sequence,
fingerprints, iris structure and coloration, appearance, etc.
Biometric Validation refers to the classification of a person
using the above- mentioned characteristics. Human
physiological or morphological characteristics, which are
unique to each individual, are most frequently utilized for
authentication and privacy applications.
Biometric solutions have the ability to address a wide
variety of authentication needs for the computerized and
rapididentification of human beings. From the many biometric
information used for e- governance, e-business, and a
variety of law- enforcement operations, fingerprinting is
perhaps the most commonly used. In addition to capillary
arrangement, handprint, face, and iris, other biometric
markers also including hand print, face, and retina have
shown their utilization in a variety of scenarios. The selection
of biometric features is dependent upon the characteristics of
the application requirements, comprising effectiveness,
precision, and mostsignificantly, user friendliness.
Numerous obstacles have surfaced with the introduction of
fingerprint biometric identification systems. It is well-
established that fingerprint recognition performance
degrades owing to recurrent scarring, dampness, residual
debris, skin deflections, or perspiration, and that a consider-
able proportion of physical workers and the aging population
also have fingerprints of insufficient integrity for recognition.
Different scholars and solution providers have developed a
variety of consumer and law enforcement solutions
employing proof of identity based on physiological proper-
ties of the individual anatomy. In the biometrics area, a
number of biological and physiological characteristics have
been evaluated for their usefulness in actual privacy and
crime investigation. Notwithstanding expanding uses in
analytics and enforcement agencies, the contactless
fingerprint is currently one of the widely explored biometric
traits, and new techniques are still being devised to fully
exploit its capability.

Related Works
Cheng, Kevin H. M., [1] investigates the construction of a 3D
hand knuckle detection method and introduces, for the quite
first instance in the published literature, a 3D hand knuckle
picture repository for future exploration. It can- not be
anticipated that any practical implementation of cur-rent 3D
features extracted, such as those created for 3D palm print
as well as 3D fingerprint recognition, can retrieve the
majority of appropriate feature using 3D finger knuckle
designs. In order to reach the peak performance of 3D finger
knuckle biometric authentication, it is necessary to construct
individual feature characteristics. Another of the key
concerns associated with each new biometric imaging
modalities pertains to its distinctiveness or identity that has
never previously been examined in the biometrics commu-
nity. This work attempts to solve this issue by constructing
an individualized concept for 3D finger knuckle patterning
utilizing the most effective feature identifier.
Ramya T.N [2] offers a strong and reliable verification
system with an exceptionally significant proportion of dy-
namical correctness. Using key set values, the approach
gives distinct layouts based on the same personal data. The
benefit of the suggested solution is that even though the
blueprint is hacked, no personal data about the user’s finger-
print is released. With the worldwide COVID epidemic, the
touch - free 3D fingerprint could serve as a means to a safe
and sanitary authentication method. In the coming years,
3D-to-2D projection will employ a non-parametric unrav-
eling approach. Utilization of additional fingerprint factors,
such as sweat pores, ridge structure, etc., for fingerprinting
production. Integration of 3D fingerprint with additional
modalities, such as ear, retina, and face.
Hanzhuo Tan [3] offers a quick and precise recurrent con-
volutional network-based architecture for enhancing the co-
operation amongst contactless fingerprint scanner and tra-
ditional contact-based fingerprint scanner. The suggested
granularity perception network, together with the equivalent
proximity loss, may handle picture creation discrepancies
and procurement abnormalities directly. The empirical find-
ings reported in the past segment on two accessible to the
public collections demonstrate that the effectiveness of the
prior approaches and enterprise applications is much worse.
These encouraging findings greatly enhance the compati-
bility between contactless and contact- based fingerprints,
which may assist to the establishment of contactless finger-
print systems.
Ajay Kumar [4] created a novel method for contactless
fingerprint granularity recognition with a deep neural net-
work which contains atrous spatial pyramid pooling. The re-
peatable empirical findings provided in this research show
that the suggested design will yield in a substantial increase
in quality. This research also includes the cross-database
contactless fingerprinting assessment system, which trains
the networks through using photos gathered throughout this
research and evaluates the network’s functionality by using
two additional publicly available datasets even without fine-
tuning. These cross-database appropriate performance find-
ings given in this research also serve that will further evalu-
ate the framework’s efficacy and resilience.
Metodi P. Yankov [5] evaluates the fingerprint pictures’
similarity measurement and volatility. It was proved that
texture-based neural network models provide calculation of
entropy every pixel regardless of main sensor. Consequently,
the entropy was proven to rely exclusively on the quan-
tity of active region included in the biometric specimen.
Consequently, the biometric effectiveness of complexity-
unrestricted fingerprint identification algorithms is simplya
consequence of the region of intersection between both the
sensor and comparison specimens. Using the MI match-ing a
probe and standard for numerous public datasets, the feasible
biometric authentication technique efficiencies were then
determined.
Methodology
Requirement Gathering and Analysis: In this stage, we de-
termine the many needs for our project, such as software,
hardware, databases, and interfaces. System Design: During
the system design phase, we create a system that is simple to
understand for the end user, i.e., user-friendly. We create
several UML diagrams and data flow diagrams to bet- ter
understand the system low, system module, and execution
sequence. Implementation: During the implementationphase
of our project, we implemented several modules that were
necessary to effectively achieve the intended results at
various module levels. The system is first built in tiny pro-
grams called modules, with input from system design,and
then combined in the following step as mentioned in Fig- ure
1. Testing: The various test cases are carried out to see
whether the project module is producing the required results
within the time frame specified. After each unit has been
tested, all of the units built during the implementation phase
are merged into a system. Following integration, the com-
plete system is tested for flaws and failures. Deployment of
System: Following the completion of functional and non-
functional testing, the product is deployed in the client envi-
ronment or launched to the market. Maintenance: There area
few difficulties that arise in the client environment. Patches
have been provided to address these problems. In order to
improve the product, newer versions are published. All of
these phases are connected in such a way that developmentis
perceived as owing progressively downhill like a water fall
through the phases. The next phase is initiated only oncethe
previously defined set of goals for the previous phase have

been met and signed off on, hence the term ”Waterfall
Model.”
The contactless fingerprint recognization system goes
through the following procedures:-
1. Preprocessing- After certain images are taken as input
it goes through preprocessing technique. The input images
are cleaned by going through certain process like
transforming, cleaning and integrating of data in order to
make it ready for further process. It mainly focuses on
improving the quality of the data and to make it more
suitable for the specific data mining task. This step also
involves techniques like combining data from multiple
sources, such as databases, spreadsheets etc. It also involves
steps such as converting thedata into a format that is more
suitable for further process. Itcan be normalizing numerical
data, creating dummy variables, and encoding categorical
data. The dataset may require to preprocess to ensure a
suitable format for training CNN.
2. Image Normalization- Image normalization is a
technique that changes the range of pixel intensity values
ofa particular image. This dataset may include photographs
with poor contrast. In this step we will perform a technique
that produces a normalization of an input image grayscale or
RGB.
Figure 1: System Design
3. CNN- is a important part for image processing. These
algorithms are currently the best algorithms we can use for
for image processing Images mainly include data of RGB
combination. The computer can’t see an image, all it can
only consider is an array of numbers. Color images are
mainly stored in 3-dimensional arrays.
UML Diagrams
1. Use Case Diagram : The use case diagram of contact-
less fingerprint recognisation is given below Use case
diagrams are useful to refresent the eternal functionality
ofa particular system. It helps in gaining detailed knowledge
about overall system. It helps in defining and organizing
functional requirements in a system. They helps in
identifying actors along with their interactions with system.
There are certain components used in building use case
diagrams. Inthe below provided use case diagram there is only
one Actor.
2. Sequence Diagram: The usecase diagram of contactless
fingerprint recognisation is given below Use case diagrams
are useful to refresent the eternal functionality of a
particular system. It helps in gaining detailed knowledge
about overall system. It helps in defining and organizing
functional requirements in a system. They helps in
identifying actors along with their interactions with system.
There are certain components used in building usecase
diagrams. Inthe below provided use case diagram there is only
one Actor.
Figure 2: Use Case Diagram
Deployment diagram : A deployment diagram is a UML
diagram that represents the execution architecture of a
particular system, which includes nodes such as hardware
or software execution environments, and the middle- ware
connecting them. These diagrams are mainly used to
visualize the physical hardware and software of a system. It
helps in understanding how the system will be physically
deployed on the hardware environments. Deployment
diagram describes the purpose of overall system. There are
certain notations that are used to build deployment
diagrams. A component diagram verifies that a system’s
3. State Transition Diagram : State-transition diagrams
describe the states that an object might have, the events un-
der which objects can change its state or transition and the
activities undertaken during the whole life cycle of an object
State-transition diagrams are very useful for describing the
behavior of each and every objects present in the system The
main focus of state transition system is to visualize the state
changes or events occuring throughout the procedure.

required functionality is acceptable or not. These diagrams
mainly helps in communication purpose between the
developer and stake- holders of the system. Programmers
and developers are the most benefited one’s from this
diagram. They can use thesediagrams as a roadmap for the
implementation, allowing forbetter decision-making purpos.
Component diagram focuses on physical aspects of object
oriented classes. Component diagrams are normally class
digrams that focuses on components of system.
Figure 3: Sequence Diagram
Figure 4: Deployment diagram
Conclusion
User authentication is one of the most crucial and highly
essential aspects of providing security and privacy to the
consumers. Without the authentication principles in current
place there would be a lot of leakage of data information and
the collapse of privacy of individuals. As it represents one of
the best comprehensive and foolproof methods for verifying
a user, the adoption of biometric techniques has expanded.
In recent years, however, there has recently beena surge in
mistrust over the usage of public biometric identification
scanners, since these devices are often coated in filth and/or
pathogens. Consequently, a practical and usable con-tact list
fingerprint identification system must be developed to
advance the fingerprint identification methodology. This
research study outlines the current approaches for contact-
less fingerprint examination or assessment that have been
essential in the development of our technique for this issue,
which will be developed in future versions of this publication.
Acknowledgments
We would like to thank our Principal Dr. V.V.Dixit, Head ofthe
Department, Prof. Vina M. Lomte ,our co-ordinator Asst.Prof.
Sonal S. Fatangare and my guide Asst. Prof Nitha KPfor their
valuable advice and technical assistance.
Reference
[1] A. Kumar, ”Contactless Finger Knuckle Authentication
under Severe Pose Deformations,” 2020 8th Inter- national
Workshop on Biometrics and Forensics (IWBF), 2020, pp. 1-
6, doi: 10.1109/IWBF49977.2020.9107951.
[2]R. Kapoor, D. Kumar, Harshit, A. Garg and A. Sharma,
”Completely Contactless Finger-Knuckle Recognition using
Gabor Initialized Siamese Network,” 2020 Interna- tional
Conference on Electronics and Sustainable Com-munication
Systems (ICESC), 2020, pp. 867-872, doi:
10.1109/ICESC48915.2020.9155554.
[3] K. H. M. Cheng and A. Kumar, ”Contactless Biometric
Identification Us- ing 3D Finger Knuckle Patterns,” in IEEE
Transactions on Pattern Analysis and Machine Intelligence,
vol. 42, no. 8, pp. 1868-1883, 1 Aug. 2020, doi: 10.1109/T-
PAMI.2019.2904232.
[4]R. T. N and V. M B, ”Analysisof Polynomial Co-Efficient
Based Authentication for 3D Fingerprints,” 2020 IEEE
International Conference for In- novation in Technology
(INOCON), 2020, pp. 1-6, doi:
10.1109/INOCON50539.2020.9298341.
[5]H. Tan and A. Kumar, ”Minutiae Attention Network With
Reciprocal Dis- tance Loss for Contactless to Contact-Based
Fingerprint Identification,” in IEEE Transactions on
Information Foren-sics and Security, vol. 16, pp. 3299-3311,
2021, doi: 10.1109/TIFS.2021.3076307.
[6]H. Tan and A. Kumar, ”To- wards More Accurate
Contactless Fingerprint Minutiae Ex- traction and Pose-
Invariant Matching,” in IEEE Transac- tions on Information
Forensics and Security, vol. 15, pp. 3924-3937, 2020,
doi:10.1109/TIFS.2020.3001732.

[7]M.P. Yankov, M. A. Olsen, M. B. Stegmann, S. S. Chris-
tensen and S. Forchhammer, ”Fingerprint Entropy and Iden-
tification Capacity Estimation Based on Pixel-Level Gen-
erative Modelling,” in IEEE Transactions on Information
Forensics and Security, vol. 15, pp. 56-65, 2020, doi:
10.1109/TIFS.2019.2916406.
[8]A. Krishnan, G. R. Nayar, T. Thomas and N. A. Nystrom,
”FEBA - An Anatomy Based Finger Vein Classification,” 2020
IEEE Interna- tional Joint Conference on Biometrics (IJCB),
2020, pp. 1-9, doi: 10.1109/IJCB48548.2020.9304889.
[9]. Toygar, F.O. Babalola and Y. Bitirim, ”FYO: A Novel
Multimodal Vein Database With Palmar, Dorsal and Wrist
Biomet-rics,” in IEEE Access, vol. 8, pp. 82461-82470, 2020,
doi: 10.1109/ACCESS.2020.2991475.
[10]R. S. Kuzu, E. Pici- ucco, E. Maiorana and P. Campisi, ”On-
the-Fly Finger- Vein-Based Biometric Recognition Using
Deep Neural Networks,” in IEEE Transactions on
Information Foren- sics and Security, vol. 15, pp. 2641-2654,
2020, doi: 10.1109/TIFS.2020.2971144.
[11]Harivanto, S. A. Sudiro, T. M. Kusuma, S. Madenda and
L. M. R. Rere, ”Detec- tion of Fingerprint Authenticity Based
on Deep Learning Using Image Pixel Value,” 2020 Fifth
International Con- ference on Informatics and Computing
(ICIC), 2020, pp. 1-6, doi:
10.1109/ICIC50835.2020.9288589.
[12]Y. Zhang,C. Gao, S. Pan, Z. Li, Y. Xu and H. Qiu, ”A Score-
Level Fusion of Fingerprint Matching With Fingerprint
Liveness Detection,” in IEEE Access, vol. 8, pp. 183391-
183400, 2020, doi: 10.1109/ACCESS.2020.3027846.
[13]K. Han and X. Li, ”Application of Partial Differential
Equation Methodin Fingerprint Image Enhancement,” 2021
IEEE/ACIS 19th International Conference on Software
Engineering Research, Management and Applications
(SERA), 2021, pp. 33-38, doi:
10.1109/SERA51205.2021.9509273.
[14]M.Yang, ”Fingerprint Recognition System based on Big
Data and Multi-feature Fusion,” 2020 International
Conference on Culture-oriented Science Technology (ICCST),
2020, pp. 473-475, doi: 10.1109/ICCST50977.2020.00097.
[15]P.Poonia, O. G. Deshmukh and P. K. Ajmera, ”Adap-
tive Quality Enhancement Fingerprint Analysis,” 2020 3rd
International Conference on Emerging Technologies in
Computer Engineering: Machine Learning and In- ternet of
Things (ICETCE), 2020, pp. 149-153, doi:
10.1109/ICETCE48199.2020.9091760.

An in-depth review on Contactless Fingerprint Identification using Deep Learning

Recommended

Recommended

More Related Content

Similar to An in-depth review on Contactless Fingerprint Identification using Deep Learning

Similar to An in-depth review on Contactless Fingerprint Identification using Deep Learning (20)

More from IRJET Journal

More from IRJET Journal (20)

Recently uploaded

Recently uploaded (20)

An in-depth review on Contactless Fingerprint Identification using Deep Learning