Face Recognition based Smart Attendance System Using IoT

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 03 | Mar 2022 www.irjet.net p-ISSN: 2395-0072
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Face Recognition based Smart Attendance System Using IoT
Tippavajhala Sundar Srinivas1, Thota Goutham2, Dr. M. Senthil Kumaran3
1-2Student, Dept. Of CSE, SCSVMV (Deemed to be University), Kanchipuram, Tamil Nadu, India
3Guide, Dept. Of CSE, SCSVMV (Deemed to be University), Kanchipuram, Tamil Nadu, India
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Abstract -Face recognition basedsmartattendancesystem
using IoT is a tool for recognizing the students face while
taking attendance by using face biometrics based on monitor
camera image capturing. In our face recognition based smart
attendance project, a raspberry pi system will be able to find
and recognize human faces fast and precisely in images. The
long-established method of calling name of each student is
tedious and there is always a chance of proxy attendance. The
proposed system is based on face recognition to maintain the
attendance record of students. As the process of attendance
taking starts the system takes pictures of the attendees and
then applies face detection and recognition technique to the
given image and the recognized students are marked as
present and their attendance is updated with corresponding
time, student name and register number. We have used deep
learning techniques to develop this project.
Key Words: Biometric Attendance, Face Recognition,
OpenCV, Raspberry Pi, Camera
1.INTRODUCTION
Education institutes these days are concerned about the
consistency of students' performance. One explanation for
this decrease in student performance is the inadequate
attendance. The long-established attendance was taken
manually that is incredibly time overwhelming and
infrequently results in human error. The old technique that
uses paper sheets for taking students’ attendance will now
not be used. This project aims for computer-based student
attendance taking system that supports the institutions to
keep records of attendance. Wehaveproposedtoimplement
a "Face Recognition based Smart Attendance System Using
IoT”. The present implementation includes facial
identification that is time saving and eradicates the
probabilities of proxy attendance due to the facial detection.
This system will currently be utilized in a section during
which participation plays a vital role. Raspberry Pi, Python
and OpenCV are the basic requirements for this system. The
system implementation uses webcam as input device to
identify the face of the person in real-time.
This project on face recognition based smart attendance
system using IoT aims to replace the manual attendance
system with automated attendance system. As all the data is
stored online in this proposed system, offline registers will
become irrelevant, making the maintenance of records
easier. Nowadays attendance is considered as a primefactor
for both the students and the educational institution.
Manual attendance is considered as a time-consuming
process or sometimes it happens for the teacher to miss
someone or students may answer multiple times on the
absence of their friends (proxy attendance).
Biometric attendance is automated method of verifying or
recognizing the identity of a living person on the basis of
some physiological characteristics, such as fingerprints or
facial features, or some aspects of the person's behavior.
Since biometric systems determine someone by biological
attributes, they are tough to forge. Face recognition is one
among the few biometric ways that has the veracity of a
physiological approach while not being intrusive.
Face Recognition is a type of biometric software that maps
an individual’s facial featuresmathematicallyandstores it as
a face print. The system uses deep learning techniques to
compare a live capture or digital image to the stored face
print in order to verify an individual’s identity. Once the
recognized face matches a stored image, attendance is
marked in corresponding excel sheet for that person. The
other reason for taking face recognition as biometric
parameter is this technology reduces the physical touch of
objects/records providing a contagious-by-touch free
environment which the whole world is adopting these days.
Automated attendance system using machine learning
approach automatically detects and recognizes face and
marks attendance which saves time and maintains a record
of the collected data.
1.1 Objectives
Our primary objective is to help the lecturers/teachers,
improve and organize the process of tracking and managing
student attendance. Additionally, this:
• Provides a valuable attendance service for both
teachers and students.
• Increases security and proxy attendance won’t be
possible.
• Provides error free, automated and reliable
attendance report.
• Uses face recognition technology which reduces the
physical touch of objects/records providing a
contagious-by-touch free environment.

2. Problem Statement
The present system of taking attendance is either bymanual
or by using finger impression as biometric parameter.
Manual /Traditional attendance is usually taken by calling
students by name which takes a lot of time and has a chance
of errors and proxies which makes the analysis of student
performance imprecise. The maintenance of record for this
type of attendance is time consuming and resource
consuming. Attendance by using finger impression as
biometric parameter is done by taking finger print as
biometric parameter which is in use in many places, which
may not be a time-consuming process compared to manual
attendance, however,keepingtherecentpandemic inmindit
is not safe to touch the finger print recognition sensor
repeatedly without a huge time gap. Also, this type is high
maintenance. So, there is a need for new attendance system
which has no lecturer interference and has a contactless
device.
3. Proposed System
The proposed system is designed to capture the face of each
student and to store it in the database for their attendance.
The face of the student needs to be captured in well-lit room
so that student's face features can be detected, the seating
and the posture of the student need to be recognized. With
this system, there is no need for the teachertomanuallytake
attendance in the class because the system records a video
and through image processing/image training the face is
recognized and the attendance database is updated in a
spreadsheet. The proposed system uses Raspberry Pi as
computer and a webcam for capturing the images.
3.1 Methodology
Facial recognition Methodologyisbeingwidelyusedinmany
projects as it has many advantages. There is requirement of
data for the system in order to trace and track the individual
and mark his/her attendance. The data is loaded by
assigning each individual’s image with a corresponding id
and name. Once the system starts, the option oftakingimage
is available for which the pre-requirement is the input of id
and name. More than 100 images will be taken in gray
format using OpenCV. These images will be the input for
Haar cascade. Haar Cascade codes the pictures into binary
code after converting them into binary image. Once the
system is given input, it is trained by clicking on train image
option available on the screen using a file called Trainer.yml
which is written in human readable data serialization
language. The features of the facewill bedetectedandstored
for further actions. The dataset has to be created in the
above said manner to further recognize the faces when
needed. Track images option is used for detecting and
recognizing the faces of individuals. After detecting the face
of each individual, attendance will be markedinspreadsheet
along with the corresponding date and time.
3.2 Algorithm
Fig -1: Depiction of the flowchart architecture and process
of the designed system
The proposed system works in the above shown way. Once
the system is activated it asks for the input whicharethelive
image of student, Student name and Id. After storing and
training the system will be ready to be used that is to track
images and produce attendance report.
1. Capture the Student’s Image
2. Apply Haar Cascade (Face Detection)
3. Extract the Rectangular Bounding Box
4. Convert to gray scale, apply histogram equalization and
Resize to 100x100
5. if Updating Database then
Store in Database
else
Apply LBPH (For feature Extraction)
end if
Post-processing
The system records a short video as input and using image
processing/image training the face is recognized and the
attendance database is updated in a spreadsheet in the way
it is shown in the above pseudo code.
3.3 Architecture
Raspberry Pi 3 Model B V1.2 is used in the project as remote
device that acts as computer for the system which is
connected to power supply by using an adapter. The device
is supplemented with a webcam and memory card to take
the images and store the contents of Pi respectively.

Fig -2: Depiction of Architecture of the project
4. Project Description
Running the project:
• Raspberry Pi is given power supply using the adapter.
• Raspberry Pi and Computer are connected to the same
WLAN.
• Advanced IP Scanner is used to find the IP Address of pi.
• The IP Address is used to log into raspberry pi using VNC
Viewer
Dataset creation:
• There are two slots to enter the ID number andnameofthe
student respectively.
Fig -3: Attendance portal with the slots to fill Name and ID
• Image taking: Image is captured using definition camera
and the image is saved. For a better accuracy, multiple
number of images will be taken. This process is done using
the ‘take image’ option. A message will be displayed in the
notification box once the image is successfully captured and
saved. For all the said process external webcam is used
which is connected to raspberry pi.
Fig -4: Taking image of the user to store in database
• Training: After the taking the image, by clicking on ‘train
image’, the image will be saved along with the entered
student ID number and name. A message will bedisplayed in
the notification box once the image is successfully trained.
Fig -5: Dataset of trained images
Attendance taking:
If the dataset is not created, the process stated above has to
be followed. If dataset is created and attendance is to be
taken:
• By clicking on track images camera startscapturingimages
and detects the face of the student, thereby marking his/her
attendance in the spreadsheet with the corresponding time
and date, ID number and name. Once attendance is taken
pressing ’q’ closes the camera.
• Haar cascades is used to detect and recognize thefacesand
it uses machine learning techniques in which a function is
trained from a lot of positive and negative images. This
process in the algorithm is feature extraction.
• Quit option is given to close the portal.

Fig -6: Face Recognition
• Notification box is given to display the corresponding
message as a consequence of the action of user.
Fig -7: Message showing that attendance is taken
successfully
5. Result
Face Recognition based Smart Attendance System Using
Internet of Things is simple for usage and works efficiently.
The system works automatically once the registration of
individual student is completed and dataset is created.
The project results the following:
• Time-saving
• More efficient
• Real-time
• Precise
• Automatic report in spreadsheet
• Online updation is easy
Fig -8: Attendance is saved in excel sheet as shown
6. Conclusion
This paper on face recognition based smart attendance
system using IoT features one of the best waysofattendance
marking system which is time-saving, more efficient, real-
time, precise, gives automatic report in spreadsheet, makes
online updation easy. The system has been implemented
using Raspberry Pi, Webcam, OpenCV, Haar cascade and
python. Haar cascade, one among the finest face detection
algorithmic program is used to confirm the standard of the
system. Face Recognition based Smart Attendance System
Using IoT is simple for usage and works efficiently. The
system works automatically once the registration of
individual student is completed and dataset is created. The
project is designed addresses many of the issues of existing
manual systems and finger print based biometric system.
Face recognition concept to mark the attendance of student
and makes the system better and efficient. This project can
substitute all other attendance systems and performs
efficiently. The Automated Classroom Attendance System
helps in increasing the accuracy and speed ultimately
achieving precise attendance to meet the needforautomatic
classroom evaluation. The purpose of the paper to reduce
errors and human effort in traditional attendance taking is
achieved via face recognition based attendance system. The
result shows the potential of our system to deal with the
problem stated in chapter 2
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Biographies
Tippavajhala Sundar
Srinivas1 is pursuing B.E
(CSE) in SCSVMV (Deemed
to be University),
Kancheepuram.
Thota Goutham2 is
pursuing B.E (CSE) in
SCSVMV (Deemed to be
U
niversity),Kancheepuram.
Dr. M. Senthil Kumaran3 is
Associate Professor in
Computer Science and
Engineering department in
SCSVMV (Deemed to be
University),Kancheepuram.

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