Face Recognition System and its Applications

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 02 | Feb 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 455
Face Recognition System and its Applications
Shantanu Shahi1, Balveer Singh2
1Research Scholar, Faculty of Science & Engineering, PK University, Shivpuri, India
2Professor, Department of Computer Science & Engineering, PK University, Shivpuri, India
---------------------------------------------------------------------***----------------------------------------------------------------------
Abstract - Face Detection has one of the hottest topics of
research and has many security and business applications in
the field of image processing and pattern analysis. Availability
of feasible technology as in addition to the growing demand
for reliable security systems in the world today has been an
encouraging many scientist to develop new methods for
capturing of facial knowledge. A large number of scientists do
different work for face detection from different fields such as
image processing, neural networks, computer vision,
psychology, computers graphics and pattern analysis and has
also been increasingly accepted by the public for use in
identification, security and law enforcement.
The face recognition system has been described in three parts.
The first describes the differences such as total aggregation,
subtraction process and hybrid process. The second describes
the application with examples, and finally the third discuss
about future research in the field of face recognition.
Key Words: Computer Vision, Holistic Matching Methods,
Feature-based Methods, Hybrid Methods
1. INTRODUCTION
Several face recognition algorithms and systems have
emerged submittedandmadesignificantprogressinlasttwo
decades. The performance of the face analysis system has
developed a new height in the case of recent developments.
However, a much work has been left to fulfill the need of
further improvement in the face recognition system. Some
environmental challenges changes in lighting, bodyposition,
facial expressions, etc. Performance of facial analysis the
system is directly related to theamountofchangeseenin the
portrait. If we can eliminate these effects, it provides better
face recognition results which lead a more reliable system
[1].
The main criteria which can be producing a better result are
listed below.
1.1 Illumination
The images of human face captured in different lightening
condition like image taken in sun light, image taking in a
room and image taking in night with different type of
lighting will produce different images of same person. This
may or may not weaken some of the facial featurescausetoo
bright or too dark objects in images. These imagesproducea
different attributes of face parameters and decrease the
performance of face recognitions system. (Fig. 1.)
Fig -1: Same image with different illumination
1.2 Head Position
In most cases face recognition systems are trained from the
images in which whole face is visible but, in many cases,
when we want to recognize the face of an unknown identity,
it is not necessary that his/her face is fully captured in
capturing device. Therefore, it is necessary that the image of
a person will be taken in front of where the personislooking
the camera.
1.3 Facial expression
As mentioned earlier, most face recognition algorithms are
standard and neutral portrait. Facial accessories such as
glasses, facial hair (beard and stubble), and emotional
expressions such as laughter, smile, grin, change some it can
affect facial symptoms and classification. Best for automatic
to overcome this problem, a face recognition system is
desired, for example by modifying it feature selection.
1.4 Occlusion
In general, people can know others even if they wear
sunglasses and scarf. This is a challenge for automatedfacial
recognition systems. Designed to replace the human brain.
Another object, partial closureofperson'sface,sunglasses or
scarf is a common problem with many facials analyzes
application. These barriers cover some of the facial features
and thus affect some of the facial features. The performance
of the portrait may deteriorate.Manywaystosolvethisissue
by splitting the closed parts of the image apart and ignoring
them corresponding activity.

1.5 Inter-Class Similarities
It is not impossible to distinguish identical twins, but
sometimes it's a tough job for parents. Therefore, similarity
between classes is not just a challenge although it is a
biometric authentication technology; italsofacesthehuman
brain. That's the problem to distinguish between two
different subjects with very similar characteristics. Among
many cases multi-biometric techniques such as combining
face and fingerprint recognition increase performance.
2. FACE RECOGNITION METHODS
In the 1970s, facial recognition was seen as a two-
dimensional cognitive problem[2].Usedtoidentifydistances
from key points of facial recognition, such as measuring
differences in facial expressions. But the familiar face
recognition should be automatic. Face recognition is a
difficult but elaborate issue that appeals to researchers from
diverse backgrounds: psychiatry, cognitive impairment,
neural networks, computing view, and computer graphics.
In general, face recognition consists of two (2) stages,
registration and identification / verification, as shown in Fig.
1. There are several modules,which areimagedetection,face
recognition, training, knowledge and identification.
Fig -2: Block Diagram for the Face Recognition System
The list of face recognition methods are follows:-
1. Holistic Methods
2. Feature-based Methods
3. Hybrid Methods
2.1 Holistic Methods
In thisapproach, total face area is calculatedastheinputdata
for the face capture system. One of the best examples of
holistic approaches is eigenfaces [8] (the most widely used
facial recognition),keynoteanalysis,segregationanalysis[7],
and independent assessment, etc.
Fig -3: Face recognition method

2.2 Feature-based Methods
In this method features which are necessary for face
recognition are extracted. A major challenge of the delete
feature is the "back" feature, which is when the system tries
to recover features that are not visible due to major changes,
such as the head pose when we match the front image with
profile picture. [5].
2.3 Hybrid Methods
Hybrid face recognition technology combines holistic and
featureextraction methods. Usually 3D graphics are usedfor
mixing. The image of the face is captured in 3D, allowing the
system to record the curves of the eye, such as the image of
the chin or forehead. Even the face in contours can be used
because the system uses depth and measuring axes, which
provide enough information to create the whole face. 3D
technology generally does this: capture, locate, measure,
represent and compete. Capture - Capture faces by copying
photos or capturing them in real time. Position - Determine
the position, size and angle of the head. Measurements -
Measure each curve of the face to model, carefully observing
the outside of the eye, the inside of the eye, and the angle of
the nose. Representation- converting models into numbers-
digital representation and face-to-face comparison -
comparing data received with faces in existing data.
If you want to compare 3D images with existing 3D images,
there is no need to make any changes. Most, however,images
are rendered in 2D, which requires some modification to the
3D image.
3. FACE RECOGNITION APPLICATIONS
Face recognition system may be very useful in human-
computer interaction, virtual reality, data recovery,
multimedia, computer entertainment, information security,
and more. Other. Work Procedures, Medical Records,
Internet Banking, Biometrics, such asPersonal Information -
Passports, Driver's License, Automatic Self-Assessment -
Border Control, Policy Private, e.g. Video Surveillance,
Investigations, Personal Security - Driver Surveillance, and
Home Video Surveillance.
3.1 Face Identification
Facial recognition teachespeoplethroughfacial expressions.
Face recognition creates an authorized account rather than
just checking if an ID card (ID) or key is valid, or if the user
knows a unique identifier (Pins) or password. The following
is an example.
Eliminate the national electoral balance because there is
more than one election. Face recognition is directly
compared to the face of voters without the use of a different
ID number. When two faces are similar in question, then it is
necessary to differentiate between the persons.
3.2 Access Control
Facial recognitions systems are very useful in access control
applications like using computer access. The size of the
crowd to identify is small. The shape of the face is also taken
in a natural way, e.g. Front and interior lighting.
3.3 Security
Today, more than ever today, safety is a major concern at
airports, as well as in airport office workers and passengers.
Airport defense systems using face recognition technology
have been used in many airports.
Anyone who is accredited by the system will be further
investigated by the public safety authorities. To prevent
others from exchanging information or exchanging
information with others when an authorized person leaves
the computer terminal for a short period of time, the user
will be constantly monitored whether the person in front of
the computer screen or at the user is the same person
authorized who is logged in.
3.4 Image database investigations
Search for photo databases of licensed drivers,beneficiaries,
missing children, immigrants and booking authorities.
3.5 Proof of identity
Elections, financial services, e-commerce, newborn
identification, national ID card, passport, working ID card.
3.6 Surveillance
Like the security application in public places, monitoring
user satisfaction with face recognition is less or less. White
lighting, face guidance, and other classifications all make
using face recognition for weather monitoring a daunting
task. Below are some examples of facial observations.
To upgrade the city’s surveillance system in London
Newham City, 300 cameras were connected to a closed-
circuit television (CCTV) control room. The city council says
the device has helped reduce crime by 34 percent since its
inception. There are similar ordinances in Birmingham, UK.
In 1999, Visionics received a contract from the National
Institute of Justice to develop CCTV smart devices.
4. CONCLUSIONS
In this research paper, we present the concepts of facial
recognition and their applications. This research paper can
give the reader a better understandingoftheprocessandthe
use of face recognition system. Different type of face
recognitions algorithms and techniquesarediscussedinthis
paper.

In the future, two dimensional and three dimensional face
recognition and major applications such as student ID card,
electronic commerce, driving license, aadhar card and voter
ID card is a viable option. Hard work in face recognition and
this topic needs further research.
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