Post on 03-May-2023
e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:04/Issue:05/May-2022 Impact Factor- 6.752 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
[5648]
STUDENT ATTENDANCE MANAGEMENT SYSTEM
Harsh Tanwar*1, Rithik Seth*2, Gulshan*3, Sahil*4 *1,2,3,4HMR Institute Of Technology & Management, New Delhi, India.
ABSTRACT
Attendance management is important to every single organization; it can decide whether or not an organization
such as educational institutions, public or private sectors will be successful in the future. Organizations will
have to keep a track of people within the organization such as employees and students to maximize their
performance. Managing student attendance during lecture periods has become a difficult challenge. Nowadays,
many applications such as video monitoring/surveillance system, human-computer interaction, door access
control system and network security use biometric authentication. One of the biometric identification is using
fingerprint. it is considered to be the best and fastest method because every person has unique fingerprint and
does not change in one's lifetime. Fingerprint recognition is a mature field today, but using face recognition
technique is still better to be applied in capturing the present of the student in the class. Other advantages using
face recognition are knowing the attitude of students in class such as students readiness or interestedness in
lecture. This paper discusses a method for managing student attendance system in classroom using multiple
facial images for classifying the facial objects. From the experiments conducted by involving 11 students
situated in classroom setting, it results in 127 out of 135 successful faces recognition. Recognition rate is about
95%.
Keywords: Attendance Management Systems
I. INTRODUCTION
Due to student’s interest in classrooms, and whose is the largest union in the study environment of university
or institution, so recording absence at a department having a large number of students in a classroom is a
difficult task and time-consuming. Moreover, the process takes much time, and many efforts are spent by the
staff of the department to complete the attendance rates for each student. So in many institutions and academic
organizations, attendance is a very important criterion which is used for various purposes. These purposes
include record keeping, assessment of students, and promotion of optimal and consistent attendance in class.
As long as in many developing countries, a minimum percentage of class attendance is required in most
institutions and this policy has not been adhered to, because of the various challenges the present method of
taking attendance presents. The process of recording attendances for students was in the form of hardcopy
papers and the system was manually done. Besides wasting time and taking efforts for preparing sheets and
documents, other disadvantages may be visible to the traditional one due to loss or damage to the sheets-sheet
could be stolen. The developed system considers as an alternative to the traditional one, it is easy, fast and
reliable than the traditional one, especially after the development of information technology and its usage by
educational institutions. Therefore, the design of student attendance system has a significant reality meaning.
The system is a Web-based application developed for daily student attendance in departments within the
university. It facilitates access to the attendance of a particular student in a particular class. This system will
also help in generating reports and evaluating the attendance eligibility of a student. The system is not only
improving the work efficiency, students’ study and development, but also can save human and material
resources. Keeping the valid and correct student attendance record in traditional face-to-face (F2F) class
setting, the faculty staffs should have a proper mechanism. They have to verify and maintain or manage that
attendance record on regular basis. They have difficulties, especially in classes attended by a large number of
students. The manual system also need time longer for reporting the average attendance of each enrolled
student. On the other hand, applying the automated attendance system may reduce the administrative burden
of its staff. Therefore, this paper describes on how we can manage student’s attendance in class using face
recognition technique automatically from the digital images captured in classroom setting.
II. RELATED WORK
System which introduces an attendance marking system, integrates computer vision and face recognition
algorithms into the process of attendance management. The system is implemented using a non-intrusive
e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:04/Issue:05/May-2022 Impact Factor- 6.752 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
[5649]
digital camera kept fixed on a classroom, which scans the room, detects and extracts all faces from the acquired
images. After faces have been extracted, they are compared with an existing database of student images and on
successful recognition a student attendance list is generated and saved on a database. This paper addresses
problems such as real time face detection on environments with multiple objects as well as social and
pedagogical issues with the applied techniques.
When a person wants to enter the access control system, the video camera take photos of the person. Then the
face is detected within a short time. The identity information in the card is compared to the information from
the database. If the identity information and the face data are matched with the information from the database
and attendance is recorded.
TECHNOLOGY USED:
1. Tkinter (Whole GUI): Tkinter is the standard GUI library for Python. Python when combined with Tkinter
provides a fast and easy way to create GUI applications. Tkinter provides a powerful object-oriented interface
to the Tk GUI toolkit.
Creating a GUI application using Tkinter is an easy task. All you need to do is perform the following steps −
Import the Tkinter module.
Create the GUI application main window.
Add one or more of the above-mentioned widgets to the GUI application.
Enter the main event loop to take action against each event triggered by the user.
2. OpenCV for taking images and face recognition: OpenCV is a great tool for image processing and
performing computer vision tasks. It is an open-source library that can be used to perform tasks like face
detection, objection tracking, landmark detection, and much more. It supports multiple languages including
python, java C++ (cv2.face.LBPHFaceRecognizer_create()): We used the cv2. face. LBPHFaceRecognizer_create
to train our face recognizer on the CALTECH Faces dataset and obtained 98% accuracy, a good start in our face
recognition journey.
Introduction
LBPH (Local Binary Pattern Histogram) is a Face-Recognition algorithm it is used to recognize the face of a
person. It is known for its performance and how it is able to recognize the face of a person from both front face
and side face.
e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
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All images are represented in the Matrix formats, as you can see here, which are composed of rows and
columns. The basic component of an image is the pixel. An image is made up of a set of pixels. Each one of these
is small squares. By placing them side by side, we can form the complete image. A single pixel is considered to
be the least possible information in an image. For every image, the value of pixels ranges between 0 to 255.
This image here is 32 pixels wide and 32 pixels high.
And when we multiply 32 by 32, the result is 1024, which is the total number of pixels in the image. Each pixel
is composed of Three values are R, G, and B, which are the basic colours red, green, and blue. The combination
of these three basic colours will create all these colours here in the image so we conclude that a single pixel has
three channels, one channel for each one of the basic colours.
Images & Pixel
All images are represented in the Matrix formats, as you can see here, which are composed of rows and
columns. The basic component of an image is the pixel. An image is made up of a set of pixels. Each one of these
is small squares. By placing them side by side, we can form the complete image. A single pixel is considered to
be the least possible information in an image. For every image, the value of pixels ranges between 0 to 255.
This image here is 32 pixels wide and 32 pixels high. And when we multiply 32 by 32, the result is 1024, which
is the total number of pixels in the image. Each pixel is composed of Three values are R, G, and B, which are the
basic colours red, green, and blue. The combination of these three basic colours will create all these colours
here in the image so we conclude that a single pixel has three channels, one channel for each one of the basic
colours. As now we have some understanding of images and pixels, now it will be easier to understand the
intuition behind the LBPH algorithm. So let’s get started with the intuition of the LBPH algorithm
e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
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[5651]
LBPH(Local Binary Patterns Histograms)
Let’s start by analyzing a matrix that represents a piece of the image. And as you learn earlier, an image is
represented in these formats. In this example, we have three rows and three columns and the total number of
pixels is nine. Let’s select the central pixel here, value eight, and apply a condition. If the value is greater or
equal to 8, the result is ‘1’ otherwise, if the value is less than eight, the result is zero. After applying the
conditioner, the matrix will now look like this.
The basic calculation of this algorithm is to apply this condition, selecting the centre element of the matrix. Now
we need to generate a binary value.Binary value = 11100010 . The algorithm will start applying the condition
from the top left corner element goes up to the 1 element of the 2nd row think like it is making a circle like this.
e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science
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After converting the Binary value to the decimal value we get Decimal Value = 226. It indicates that all these
pixels around the central value equal to 226.
This algorithm is robust when it comes to lightning. If you put a flashlight on the image, the value of the pixels
will increase. Higher the values the brighter the image and when values are lower darker the image will be. For
this reason, this algorithm has good results in light and dark images because when the image becomes lighter
or darker, all the pixels in the neighbourhood here will be changed. After putting the light on the image the
matrix will look like this. After applying the above condition we will get the binary value the same as above
i.e 11100010
Final output of image :
LOCAL BINARY PATTERN AND HISTOGRAM:
• Suppose we have a facial image in grayscale.
• We can get part of this image as a window of 3x3 pixels.
• It can also be represented as a 3x3 matrix containing the intensity of each pixel (0~255).
• Then, we need to take the central value of the matrix to be used as the threshold.
• This value will be used to define the new values from the 8 neighbors.
• For each neighbor of the central value (threshold), we set a new binary value. We set 1 for values equal or
higher than the threshold and 0 for values lower than the threshold.
• Now, the matrix will contain only binary values (ignoring the central value). We need to concatenate each
binary value from each position from the matrix line by line into a new binary value (e.g. 10001101). Note:
some authors use other approaches to concatenate the binary values (e.g. clockwise direction), but the final
result will be the same.
• Then, we convert this binary value to a decimal value and set it to the central value of the matrix, which is
actually a pixel from the original image.
• At the end of this procedure (LBP procedure), we have a new image which represents better the
characteristics of the original image.
e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:04/Issue:05/May-2022 Impact Factor- 6.752 www.irjmets.com
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[5653]
CSV, Numpy, pandas, datetime etc:
NumPy can be used to perform a wide variety of mathematical operations on arrays. It adds powerful data
structures to Python that guarantee efficient calculations with arrays and matrices and it supplies an enormous
library of high-level mathematical functions that operate on these arrays and matrices.
Pandas is an open source Python package that is most widely used for data science/data analysis and machine
learning tasks. It is built on top of another package named Numpy, which provides support for multi-
dimensional arrays
Date and time Python library defines a function that can be primarily used to get current time and date. now()
function Return the current local date and time, which is defined under datetime module. Parameters : tz :
Specified time zone of which current time and date is required.
Face Detection with Haar Cascade
Face Detection, a widely popular subject with a huge range of applications. Modern day Smartphones and
Laptops come with in-built face detection softwares, which can authenticate the identity of the user.
It is an Object Detection Algorithm used to identify faces in an image or a real time video. The algorithm uses
edge or line detection features proposed by Viola and Jones in their research paper “Rapid Object Detection using
a Boosted Cascade of Simple Features” published in 2001. The algorithm is given a lot of positive images
consisting of faces, and a lot of negative images not consisting of any face to train on them.
A majority of these features won’t work well or will be irrelevant to the facial features, as they will be too
random to find anything. So here they needed a Feature Selection technique, to select a subset of features from
e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:04/Issue:05/May-2022 Impact Factor- 6.752 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
[5654]
the huge set which would not only select features performing better than the others, but also will eliminate the
irrelevant ones. They used a Boosting Technique called AdaBoost, in which each of these 180,000 features
were applied to the images separately to create Weak Learners. Some of them produced low error rates as they
separated the Positive images from the Negative images better than the others, while some didn’t. These weak
learners are designed in such a way that they would misclassify only a minimum number of images. They can
perform better than only a random guess. With this technique, their final set of features got reduced to a total of
6000 of them.
THE ALGORITHM HAS FOUR STAGES:
1. Haar Feature Selection
2. Creating Integral Images
3. Adaboost Training
4. Cascading Classifiers
A]&B]Edge feature
C]&E] Line feature
D] Four rectangle feature
e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:04/Issue:05/May-2022 Impact Factor- 6.752 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
[5655]
III. CONCLUSION
Attendance management is significant to all organizations such as educational institutions. It can manage and
control the success of any organization by keeping track of people within the organization such as students to
maximize their performance. The proposed system offers the process of monitoring attend students, it aims to
help the teacher in the classroom or laboratories to manage and record students' presence electronically and
directly without the need to list on paper so it will save time and effort. The system can analyze the data and
displays statistics about the student‘s absences, printing report about absence percentages and students
warnings for the specified period. The developed system easy to use and friendly that has an attractive and
simple GUI is design so that insertions, deletions, and changes of data can do easily without interacting with the
tables. The application’s test case revealed that the system is working exciting and is ready to use to manage
students attend for any department of the University, College or Institute. Since our system is modular and can
extend effortlessly, the future work ambitions are to make the system takes attendance by other methods such
as face recognition and using Biometrics (fingerprint) techniques, NFC mobile devices, or RFID Systems.
Furthermore, we would like to make the system to manage and record the attendance for the staff of the
university.
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[4] Jian Xiao, GugangGao, Chen Hu, HaidongFeng “A Novel Framework for Fast Embedded Face Detection
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