ONLINE PROCTORING SYSTEM

ABSTRACT:-

Distance and online learning (or e-learning) has become a norm in education and training  due to a variety of advantages such as efficiency, flexibility, affordability and ease of use. insulation requirements. However, monitoring participants and students during classes, especially exams, is a major challenge for online systems due to the lack of physical presence. Methods and technologies need to be developed that provide robust tools for detecting unfair, unethical and illegal behavior during the classroom. classes and exams. We propose in this article a novel online surveillance system that uses deep learning to continuously monitor physical locations without the need for a physical monitor. The system uses biometric approaches such as face recognition with the face detector HOG (Histogram of Oriented Gradients) and the  face recognition algorithm OpenCV. In addition, the system includes flicker detection to detect stationary images. To ensure fairness during exams, the system can also recognize devices such as mobile phones, laptops, iPads and books.

EXISTING SYSTEM:-

In existing system there is an additional monitoring system that has introduced a multimedia analysis system, which has detection, telephone code, text recognition, speech recognition, covariance function.together and in case of entropy by various mechanisms used such as noise detection, valid gaze detection failure  and other features,  the user  is flagged as a result to eliminate false alarms and confirmed fraud cases. A binary SVM classifier is used where the audio frequency is divided into 16 different channels and speech is considered a positive pattern and other categories of speech are purely negative patterns.Analysis of the visible range of the screen, the screen is reported by calculating the convex helmet of the large region.

PROPOSED SYSTEM:-

This project’s aim is to create Web application that can be used to conduct and manage online exams and have better security. The software will reduce the chance of student malicious activities and creates a secure exam environment. This software will have different levels of candidate authentication like Username and Password and face recognition. Teacher will have a separate login to this software which he/she can use to create classes and exams. The no. of students in the class, schedule of exam, exam time and admission of students the course will remain in hands of the respective teacher.. Students should undergo face recognition to login on software successfully. The teacher will  schedule the exam along with uploading the questions. Student can access the exam . Each student will get randomly shuffled set of questions which he/she has to solve in allotted time. This software will have “Online Remote Proctoring” which will monitor students throughout the examination with the help of  webcam (for laptops). Using Machine Learning The proctor will be constructed to detect head  movement of candidate, face, If it detects any kind of vulnerability, the software will give 10 warnings after which the exam will be automatically submitted by software.

MODULES:-

• Registration: Students who register in a portal for the first time submit their personal data, their ID card and their photo, which is stored in the database and verified using their photo before the exam.
  
  
• Face recognition: A webcam is installed in the a student’s computer or front camera, when the student takes a test on a face recognition recognizes the student  and if the face matches the stored facial image, the student is verified and allowed to take the exam.During the exam, the student’s image is continuously captured and if the face does not match the stored image, their record is saved in the database. Multiple face detection: If there is more than one person  in the picture, this is also recorded in the database.
  
  
• Head Position Detection: For MCQ-based exams that do not require pencil and paper, students’ head position is analyzed and if it appears that a student is looking at the other side of the screen, your dataset is also analyzed be saved.
  
  
• Cell Phone Detection – If a student is found with a cell phone, this will also be recorded as bad practice in the database.Misconduct and will be logged.

APPLICATION:-

• School/Collage exam
• GMAT,CAT exams
• Any company exam

HARDWARE AND SOFTWARE:-

HARDWARE: –

• Processor: Intel Core i3 or more.
• RAM: 4GB or more.
• Hard disk: 250 GB or more.

SOFTWARE:-

• Operating System : Windows 10, 7, 8.
• Python 
• Anaconda
• Spyder, Jupyter notebook.
• MYSQL
• Backend server:- Flask python

Frontend:- HTML, CSS, Javascript