Authors: Prof. A. A. Bamanikar, Aniket Patil, Dheeraj Singh, Mayuresh Kumbhar, Vaishnavi Badhe
DOI Link: https://doi.org/10.22214/ijraset.2023.52692
Certificate: View Certificate
With the expansion of Internet and technology over the past decade, E-learning has grown exponentially day by day. Cheating in exams has been a widespread phenomenon all over the world regardless of the levels of development. Therefore, detection of traditional cheating methods may no longer be wholly successful to fully prevent cheating during examinations. Online examination is an integral and vital component of E-learning. Student’s exams in E- learning are remotely submitted without any monitoring from physical proctors. As a result of being able to easily cheat during e-exams, E-learning universities depend on an examination process in which students take a face-to-face examination in a physical place allocated at the institution premises under supervised conditions, however these conflicts with the concept of distant E-learning environment.
I. INTRODUCTION
Today’s pandemic situation has transformed the way of educating a student. Education is undertaken remotely through online platforms. In addition to the way the online course contents and online teaching, it has also changed the way of assessments. In online education, monitoring the attendance of the students is very important as the presence of students is part of a good assessment for teaching and learning. Educational institutions have adopting online examination portals for the assessments of the students. These portals make use of face recognition techniques to monitor the activities of the students and identify the malpractice done by them. This is done by capturing the students’ activities through a web camera and analysing their gestures and postures. Image processing algorithms are widely used in the literature to perform face recognition. Despite the progress made to improve the performance of face detection systems, there are issues such as variations in human facial appearance like varying lighting condition, noise in face images, scale, pose etc. that blocks the progress to reach human level accuracy. The aim of this study is to increase the accuracy.
II. LITERATURE SURVEY
III. PROPOSED SYSTEM
We proposed a system that includes, among other things, the candidate’s surrounding environment, a liveliness check, and a face comparison of the candidate with his or her image. The evaluator can use this application to double-check the candidate’s activity at any time during the examination. The following characteristics are included in the online test that was built for taking an online test, when compared to the current system, the suggested system will be faster and more efficient Because the simulator performs the calculations and evaluations, the result will be very precise and correct, and it will be declared in a very short period of time. The proposed solution is extremely safe because there is no risk of question paper leaking because it is entirely dependent on the administrator, the records of applicants who appeared and their marks are maintained and can be backed up for future use.
IV. MOTIVATION
The basic idea of this project is to develop an application which can provide security as well as to develop a software for automatic MCQs exam evaluation We get motivated of existing system. We have to match user object with database image using Scale invariant feature transform. In that system first we have to capture the face then pre-processing on that video /image then select feature extraction and compare face object with database face and get the result for register face and then after that MCQ test will be start. The basic idea of this project is to develop an application which can provide security as well as to develop a software for automatic MCQs exam evaluation.
V. RESEARCH METHODOLOGY
Haar Cascade: It is an Object Detection Algorithm used to identify faces in an image or a real time video. This include models for face detection, eye detection, upper body and lower body detection, license plate detection etc. The algorithm uses edge or line detection features proposed by Viola and Jones. 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.
The Haar Cascade algorithm is to find out the sum of all the image pixels lying in the darker area of the haar feature and the sum of all the image pixels lying in the lighter area of the haar feature. And then find out their difference. Now if the image has an edge separating dark pixels on the right and light pixels on the left, then the haar value will be closer to 1. That means, we say that there is an edge detected if the haar value is closer to 1.
Among of all other object detection method Haar feature-based cascade classifier is the most effective method. For training the classifier of this algorithm need a lot of image of faces and without faces. Image of faces are called ‘Positive Image’ and image of without faces are called ‘Negative Image’. The value of each feature is calculated by doing the subtraction between the sum of Pixels of white rectangle and black rectangle. Haar feature is shown in figure 1.
A machine learning based face detection and recognition system using Haar cascade model is proposed to detect the faces of students for monitoring their activities during online examinations. The proposed system aids in detecting the faces in a faster manner by obtaining feature vectors from the input images. Higher accuracy can be obtained. This application can be utilized in a variety of situations, including schools, colleges, and distant online interviews. The candidate may take the exam from any location, and the evaluator may inspect the candidate at any moment while or after the exam has been finished.
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Copyright © 2023 Prof. A. A. Bamanikar, Aniket Patil, Dheeraj Singh, Mayuresh Kumbhar, Vaishnavi Badhe. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET52692
Publish Date : 2023-05-21
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here