STUDENT ATTENDANCE MANAGEMENT SYSTEM - IRJMETS

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

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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





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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.





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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





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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.





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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.





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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





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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

https://blog.paperspace.com/adaboost-optimizer/#:~:text=AdaBoost%20is%20an%20ensemble%20learning,turn%20them%20into%20strong%20ones.





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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.

IV. REFERENCES [1] VisarShehu, Agni Dika, “Using Real Time Computer Vision Algorithms in Automatic Attendance

Management Systems” ITI 2010 32nd Int. Conf. on Information Technology Interfaces, June 21-24,

2010, Cavtat, Croatia.

[2] PAN Xiang, “Research and Implementation of Access Control System based on RFID and FNNFace, 2012

International Conference on Intelligent Systems Design and Engineering Application 12 2012 2012.400

Recognition”, 978-0-7695-4608-7/11 $26.00 © 2011 IEEE

[3] Mr.Jitendra B. Jawale Ear Based Attendance Monitoring System Proceedings of ICETECT 2011, 978-1-

4244-7926- 9/11/$26.00 ©2011 IEEE

[4] Jian Xiao, GugangGao, Chen Hu, HaidongFeng “A Novel Framework for Fast Embedded Face Detection

System”, 1- 4244-1132-7/07/$25.00 © 2007 IEEE

[5] Jing Bian, Wei Du, “Research of Face Detection System Based on Skin-Tone Feature”, 978-1-61284-722-

1/11/$26.00 ©2011IEEE

[6] Nai-Jian Wang “A Real-time Multi-face Detection System Implemented on FPGA”, 2012 IEEE

International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2012)

November 4-7, 2012

[7] Xianghua Fan, Fuyou Zhang, Haixia Wang, Xiao Lu, “The System of Face Detection Based on

OpenCV”,978-1-4577- 2074-1/12/$26.00_c 2012 IEEEYohei Ishii, “Face and Head Detection for a Real-

Time Surveillance System”, Proceedings of the 17th International Conference on Pattern Recognition

(ICPR’04) 1051-4651/04 $ 20.00 IEEE

[8] Rahul Chandrasekaran, “Security Assurance Using Face Recognition & Detection System Based On

Neural Networks”, The 2005 IEEE International Conference on Neural Networks and Brain ICNN&B'05.

STUDENT ATTENDANCE MANAGEMENT SYSTEM - IRJMETS

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