attendence system face detection

1. 9/10/2022
ATTENDENCE SYSTEM THROUGH FACE
DETECTION 1

2. DIWAKAR NATH TRIPATHI (ROLL NO: 2114058360019)
RAKESH CHAURASIYA
ARBAJ ANSARI
(ROLL NO: 2114058360035)
(ROLL NO: 2114038360011)
9/10/2022
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DETECTION 2

3. 9/10/2022
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DETECTION 3

4. ABSTRACT
In the era of modern technologies emerging at rapid pace
there is no reason why a crucial event in educational sector
such as attendance should be done in the old boring
traditional way.
Attendance monitoring system will save a lot of time and
energy for the both parties students as well as the class
teachers. Attendance will be monitored by the face
recognition algorithm by recognizing only the face of the
students from the rest of the objects and then marking them
as present.
The system will be pre feed with the images of all the
students and with the help of this pre feed data the
algorithm will detect them who are present and match the
features with the already saved images of them present in
the database.
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5. Now a days many educational institutes are using a manual
monitoring system and most of the time they accidentally
loss their attendance sheet so that they cannot properly
monitor the attendance of their students .
Therefore it is important to design software which will help
these institutes to mark the attendance of the students by
face recognition which will save their time.
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6. INTRODUCTION
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The purpose of the attendance monitoring
system using face recognition is to ease
the attendance process which consumes
lot of time and efforts , it is a convenient
and easy way for students and teacher.
The system will capture the images of the
students and using face recognition
algorithm mark the attendance in the
sheet. This way the class-teacher will get
their attendance marked without actually
spending time in traditional attendance
marking
This is the project about Attendance System
Through Face detection. The biometric
technique implies determination if the images
of the face of any particular person matches
any of the face images that stored
in a database.

7. • Face detection is the first stage in the process of face analysis,face
tracking, and, most crucially, facial recognition systems which are all
subsequent steps.
• The technology is expanding at a rapid pace and it is used in
a variety of applications including device unlocking, banking, tourism,
police enforcement, building security, and others.
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8. • Face detection technology uses machine learning and algorithms
in order to extract human faces from larger images; such images
typically contain plenty of non-face objects, such as buildings,
landscapes, and various body parts.
• Facial detection algorithms usually begin by seeking out human
eyes, which are one of the easiest facial features to detect. Next,
the algorithm might try to find the mouth, nose, eyebrows, and
iris. After identifying these facial features, and the algorithm
concludes that it has extracted a face, it then goes through
additional tests to confirm that it is, indeed, a face.
• To make algorithms as accurate as possible, they must be trained
with huge data sets that contain hundreds of thousands of
images. Some of these images contain faces, while others do not.
The training procedures help the algorithm’s ability to decide
whether an image contains faces, and where those facial regions
are located.
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DETECTION 8

9. A sliding windows play an important role
in an object classification, as it allows us
to localized exactly where in an images
and object resides
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REAL TIME FACE CAPTURED BY
CAMERA WHILE SAVING THE DATA IN
DATA BASE WHILE ADDING THE
DETAIL IN ATTENDENCE

11. It create multiple copies of the same images( maximum 100 )
and if it match in the database ,it give the proper appropriate
details of the person standing in front of the camera and made
the correct attendance by capturing the face image
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12. 1.Save images to database
2.Detect faces from web cam or external
3.Match detected face to data base
4.Provide accurate information about
them
Images
Face
detection
Features
extraction
Features
extraction
Verification &
identification
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13. DIFFERENCE BETWEEN
DETECTION AND RECOGNITION
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• Face Detection: It has the objective of finding the faces (location
and size) in an image and probably extract them to be used by the
face recognition algorithm.
• Face Recognition: with the facial images already extracted,
cropped, resized and usually converted to grayscale, the face
recognition algorithm is responsible for finding characteristics
which best describe the image.

14. DIFFERENCE BETWEEN SENSOR BASED
BIOMETRIC SYSTEM AND FACE
RECOGNIZATION
Biometrics is a broad category. It describes any method of using data points from your body to
either verify your identity or verify your credentials. For example, biometrics includes fingerprint
reading, voice recognition, and even the high-tech retina scanners you’ve seen in spy movies.
While they seem like innovative practices, biometrics have been part of our daily lives for far
longer than most people realize. For example, logging into your phone or tablet with your
fingerprint has been around so long that it’s practically outdated, and some voice assistants use
voice recognition to obey your own commands rather than getting confused by everyone who
asks a question on a TV show you’re watching.
Facial recognition is actually one type of biometric as well. In the same way that
fingerprint readers check whether you have the same data points in your
fingerprint as the person who’s logging in, facial recognition looks for facial
data similarities for the same purpose. Like other biometrics, facial recognition
is a common part of our daily lives today, like logging into your phone or
authorizing an Apple Pay purchase with FaceID.
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15. ADVANTAGES
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• It is trouble-free to use.
• It is a relatively fast approach to enter
attendance
• Is highly reliable, approximate result
from user
• Best user Interface

16. Uses of Facial Recognition
• Face detection may be used for a broad range of purposes, from defense to ads. Any
examples in usage include Smartphone makers, including Apple, for public
protection.
• S. Government at airports, by the Homeland Security Agency, to recognize people
who can meet their visa criteria.
• Law enforcement by gathering mugshots can evaluate local, national, and federal
assets repositories too.
• Social networking is used for identifying individuals in photos, which also includes
Twitter.
• Business protection, as businesses may use facial recognition to access their building.
• Marketing, where advertisers may use facial recognition to assess particular age,
gender, and ethnicity
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17. Hundreds of companies have embraced face recognition. Integrating and
installing is reasonably straightforward, but it has also provided users a feeling of
utilizing a system that is more sophisticated and safer than passwords or PINs,
thereby increasing user experience.
Nonetheless, plenty is often unclear on the road to implementing what many
deem the ideal biometric approach, causing several relatively severe blunders
along the way.
Facial recognition devices are already being tested or implemented for
airport protection, and it is reported that their face print has now been
produced by more than half the United States populace. Information may
be collected and processed by a facial recognition program, and a person
does not even recognize it.
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19. REFERENCE
S
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• http://www.wordpress.org/
• http://www.academia.edu/
• http://www.stackoverflow.com/
• http://www.iproject.com
• http://www.geeksforgeek.com

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