facial recognition system is a technology capable of matching a human face from a digital image or a video frame against a database of faces, typically employed to authenticate users through ID verification services, works by pinpointing and measuring facial features from a given image.[1] Development began on similar systems in the 1960s, beginning as a form of computer application. Since their inception, facial recognition systems have seen wider uses in recent times on smartphones and in other forms of technology, such as robotics. Because computerized facial recognition involves the measurement of a human's physiological characteristics, facial recognition systems are categorized as biometrics. Although the accuracy of facial recognition systems as a biometric technology is lower than iris recognition and fingerprint recognition, it is widely adopted due to its contactless process.[2] Facial recognition systems have been deployed in advanced human–computer interaction, video surveillance and automatic indexing of images.[3] Facial recognition systems are employed throughout the world today by governments and private companies.[4] Their effectiveness varies, and some systems have previously been scrapped because of their ineffectiveness. The use of facial recognition systems has also raised controversy, with claims that the systems violate citizens' privacy, commonly make incorrect identifications, encourage gender norms and racial profiling, and do not protect important biometric data. The appearance of synthetic media such as deepfakes has also raised concerns about its security.[5] These claims have led to the ban of facial recognition systems in several cities in the United States.[6] As a result of growing societal concerns, Meta announced[7] that it plans to shut down Facebook facial recognition system, deleting the face scan data of more than one billion users.[8] This change will represent one of the largest shifts in facial recognition usage in the technology's history Facial recognition systems are employed throughout the world today by governments and private companies.[4] Their effectiveness varies, and some systems have previously been scrapped because of their ineffectiveness. The use of facial recognition systems has also raised controversy, with claims that the systems violate citizens' privacy, commonly make incorrect identifications, encourage gender norms and racial profiling, and do not protect important biometric data. The appearance of synthetic media such as deepfakes has also raised concerns about its security.[5] These claims have led to the ban of facial recognition systems in several cities in the United States.[6] As a result of growing societal concerns, Meta announced[7] that it plans to shut down Facebook facial recognition system, deleting the face scan data of more than one billion users.[8] This change will represent one of the largest shifts in facial recognition usage in the technology's history. Pleasure.

1. Group Members : IP Faculty Name :
 Divyansh Pandey-21BCS11270 Dr. Shubham Negi
 Sanskar Shukla-21BCS11373
 Jyotiraditya Pandey -21BCS11351 Supervisor Name :
 Aarya Kapoor-21BCS2487 Er. Anil Behal
 Dhruv Chodhary-21BCS11367
Group Name->ON21BCS-505_GPB_T6
FACE RECOGNITION
ATTENDANCE SYSTEM
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2. TABLE
OF
CONTENT
1 INTRODUCTION
2 LITERATURE SURVEY
3 OBJECTIVES
4 PROBLEM STATEMENT
5 METHODOLOGY
6 ALGORITHM
7 RESULT
8 REFERENCES 2

3. Every college requirean attendance system to
maintain record ofpresent student.
Face Recognition Attendance System is developed
for the Faculty to maintain attendance record.
It uses facial recognition technology to identify the
person’s facial features and automatically mark
attendance which is very fast enough than
previous method.
INTRODUCTION
3

4. LITERATURE SURVEY
Akbar, Md Sajid in [1] proposed a model of an automated attendance system.
The model focuses on how face recognition incorporated with Radio
Frequency Identification (RFID). The system keeps the authentic record of
every registered student.
Okokpujie, Kennedy O in [2], authors have designed and implemented an
attendance system which uses iris biometrics. At the time of attendance, the
system automatically took class attendance by capturing the eye image of
each attendee.
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5. LITERATURE SURVEY CONTD.
PREVIOUS WORK
This is a project done previously by students as a Second year
project at Kingston University London in 2018.
The system will be presented an image either via camera or from
memory and it must detect the number of faces on it automatically.
The second step will be the recognition part where the system will be
able to match faces from the stored dataset and compare it to the
input data from the first step.
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6. OBJECTIVES
Main objective of the research work is to detect faces of known
people and mark their attendance and store it in online storage .
This can be divided into following sub problems :
a) Reducing time wastage during conventional class attendance.
b)To find face in an image and recognize whether it is real or not .
c)To analyze the features of face recognition for making attendance .
d)To compare against known faces .
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7. PROBLEM STATEMENT
 In previous face recognition system they were taking More
database for this work so due to this many institutions have not
used to continue with this technique so for this we have used lower
resolution images and have too stored them in a cloud storage so
that it will save our storage and too it can be operated on a lower
specification PC.
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8. METHODOLOGY
Finding all the faces - We are going to use a method called Histogram of
Oriented Gradients of just (HOG) short .
Posing and projecting faces - To project a face, we are going to use the
algorithm called Face Landmark Estimation. A pattern where we identify key
points on a face, such as tip of the nose and center of the eye.
Encoding faces - To encode faces we are going to train a Deep
Convolutional Neural Network, we are going to train it to generate 128
measurements for each face .
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9. METHODOLOGY CONTD.
Training process works by looking at 3 face images at a time :
Load a training face image of known person .
Load another picture of the same known person.
Load a picture of a totally different person .
Finding the person's name from the encoding : Support Vector
Machine (SVM) is a classification technique used for the classification
of linear as well as non linear data
ATTENDANCE
DETECTION
CAMERA
STUDENT
9

10. RESULT
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11. 11

12. [1] Akbar, Md Sajid, et al. “Face Recognition and RFID Verified Attendance
System.” 2018 International Conference on Computing,Electronics &
Communications Engineering (iCCECE). IEEE, 2018.
[2] Okokpujie, Kennedy O., et al. "Design and implementation of a student
attendance system using iris biometric recognition." 2017 International
Conference on Computational Science
[3] http://eprints.utar.edu.my/2861/1/CT-2018-1503979-2.pdf
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13. [4] https://www.alibaba.com/product-detail/Original-Hik-vision-8MP-
WDRH_60752337217.html?spm=a2700.details.deiletai6.1.584755a0Lhzdop
[5]https://www.researchgate.net/publication/326986115_Face_Detection_and
_Recognition_Student_Attendance_System
[6] Lukas, Samuel, et al. “Student attendance system in classroom using
face recognition technique.” 2016 International Conference on Information
and Communication Technology Convergence (ICTC). IEEE, 2016.
REFERENCES
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14. Thank You!
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