PPT done by saveetha students on the automated attendance Mark using face Detection and Recognise

1. N.Charan Kumar, J.Sai Pavan, M.Sahindra Reddy,
Under Guidance of Prof. Dr.Senthilvadivu
AUTOMATIC ATTENDENCE MAKER
USING FACE DETECTION & RECOGNISE
PRESENTED BY

2. CONTENT
2
● Problem Statement
● Introduction
● Abstract
● Literature Survey
● Gap Analysis
● Proposed System
● Algorithm
● Prerequisites
● Dynamics Classes
● Future Scope
● Limitation
● Conclusion

3. INTRODUCTION
Many studies have shown that the attendance of
students in universities shows a falling trend over the
years. There are various factors involved in the low
attendance such as lack of interest or lack of good-
teaching skills, other extracurricular activities like
part-time jobs or availability of online content.
Therefore, our system aims to solve the problem of
lack of attendance or management related problem.
3

4. ABSTRACT
In Universities, maintaining classroom availability & scheduling time for students dynamically is hard
for respective subject, students are unable to attend classes because of classroom availability &
scheduling. If the management is done manually then it becomes a very tedious task. Our system aims
to overcome this problem by implementing algorithm which are present in modern days such HOG
(Histogram oriented gradients), Eigenfaces, fisherfaces, CNN (Convolutional Neural Network) & etc. are
one of the many algorithms used in this modern days & our system use the HOG algorithm to get the
strength of students & arrange the classroom dynamically for students according to the respective
subject, they want to attend. Our educational system will be different than the traditional one & will
be used to identify the person / human who is entering in & out from a classroom & count total
number of human / persons present in a classroom, it will not consider any objects. This count is
stored into our database like MySQL Database which is used for storing person identity. We use a
portable device i.e., Raspberry Pi which will be adjusted according to classroom infrastructure. The
Raspberry Pi is connected to either Ethernet or College WIFI so that other data like Total number of
lectures taken in a month or semester or year, how many numbers of lecture taken by a teacher in a
month or semester or year & Attendance Sheets, it will also be used to modify, delete, edit & update
the datasets stored in a Raspberry Pi and all these data will be accessed by the college localhost
computer.
4

5. LITERATURE REVIEW
5
SNo. Tittle DOI Pros and Cons
1 Counting students using OpenCVand Integration with
Firebase for Classroom Allocation.
10.1109/
ICESC48915.2020.91
55825
Pros : Accurate Student Counting, Scalability
Cons : Complex Integration, Maintenance Overhead
2 A Review of Face Recognition Technology 10.1109/ACCESS. Pros : Enhanced Security, Convenience
2020.3011028
Cons: Privacy concerns, Bias and Inaccuracy
3 Real-time implementation of face recognition
system
10.1109/ICCMC.
2017.8282685
Pros:Instant Authentication, Enhanced Security
Cons : Privacy Concerns, Cost of Implementation
4 Face Detection and Recognition System using
Digital Image Processing
10.1109/ICIMIA
48430.2020.907483
8
Pros : Accuracy, Automation, Scalability
Cons : Complexity, Resource Intensive
5 Research about human face recognition
technology
10.1109/ICTM.
2009.5412901
Pros: Biometric Authentication, Enhanced Security
Cons: Security Vulnerabilities, Privacy Concerns

6. “Dynamic Room Allocation” has been referred to as base paper. In which they have
implemented a system for dynamic allocation of classes. In the first module, they have
compared various constraint optimization techniques like classroom assignment problems and
integer linear programming and taken various constraints like a class constraint, course
constraint, no two lectures can be scheduled in the same class at the same time or no two
teachers can be assigned the same classroom at the same time etc. Next module is about the
dynamic allocation where the model which had been built in the first module is dynamically
adapted to real-time data which is the students present on a given day for a given lecture. They
have used historical data for training this model that is attendance from previous semesters.
“Our system will be using the count of students attending lectures using Raspberry Pi 4B with
its Camera Module and not from the traditional attendance ledger. This count will be stored in
the database and then its data will be used by college according to their requirement.”
GAP
ANALYSIS
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7. System Architecture (UI)
a) Initialize face recognizer.
b) Get faces and Id’s from
database folder to train
the face recognizer.
c) Save the trained data as
xml or yml file.
PROPOSED SYSTEM
The total system is divided
into 3 modules- Database
creation, Training the dataset,
Testing, sending alert
messages as an extension.
Database creation
a) Initialize the camera
b) Get user id as input
c) convert the image into
gray scale, detect the
face and
d) Store it in database by
using given input.
Testing
a) Capture the image from
camera,
b) Convert it into gray
scale,
c) Detect the face in it and
Predict the face using the
Training

8. STEPS FOR COUNTING
(ALGORITHM)
• Take the input frame from the video and remove the
background.
• HOG algorithm is used to identify humans among all the
objects.
• To mark each person for keeping track of their movement in the
frame.
• By using templates provided by OpenCV for marking
attendance of students.
• Once the person cross the plane/frame (move into or exit the
classroom), make the changes to the count. Increase the count if
entering or decrease if existing. 8

9. DYNAMICS CLASSES
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The quality of technical education depends on various factors: outcome based socially
and industrially relevant curriculum, good quality motivated faculty, teaching learning
process, industry internship, evaluation of students based on desired outcomes among
others. Therefore, it was of imperative that a revised curriculum be prepared by the
institute in order to be a competitive institute which goes to current time of change,
thus some external seminar or workshop is required for student in order to enhance
their skill and meet up the required criteria which is set or expected by the institute or
university.
Our model comes in handy in such cases.

10. PREREQUISITES
Hardware Requirement
● Raspberry Pi 4B
● Pi Module Camera
● Memory Card
Software Requirement
● Python
● MySQL
● PHP, HTML& CSS
● OpenCV
● Scikit Learn

11. FUTURE SCOPE
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Over the years, movies have fixed a futuristic fantasy in our minds that a
time will come when software would be used to recognize people by their
faces. A time when our faces will be our ID cards. With advent of facial
recognition technology, that time is already here.
Today, along with drones, AI and IoT, facial recognition technology is also
defining our millennium. Facial recognition is a biometric technology used
for authentication and examination of individuals by correlating the facial
features from an image with the stored facial database. Face Recognition is
one of the most popular applications of image analysis software and no more
considered as a subject of science fiction. Earlier, this technology was only
used for security and surveillance purposes, but it has safely transitioned to
the real world in recent times. Today, companies are pitching facial
recognition software as the future of everything from retail to policing.

12. LIMITATIONS
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Despite the benefits, there are four factors that limit the effectiveness of facial
recognition technology:
1. Poor Image Quality Limits Facial Recognition & Effectiveness
2. Small Image Sizes Make Facial Recognition More Difficult
3. Different Face Angles Can Throw Off Facial Recognition & Reliability
4. Data Processing and Storage Can Limit Facial Recognition Tech

13. CONCLUSION
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MySQL used for maintaining and storing data of the respective
college/University.The HOG algorithm serves as our base to identify faces in the
video (which is the count of students). This count is stored into the MySQL
database to further process and predict attendance. For dynamic allocation of
classrooms, the teacher decides which classroom will be allotted for student
according to the strength of student present in a classroom. Also, this model
can be used in identifying students who are bunking lectures by detecting the
faces of student who goes outside before the scheduled time of lecture. Our
model takes new attendance for every new lecture.

14. THANK YOU