Online Examination using Raspberry Pi
Online Examination using Raspberry Pi – Nowadays online exam has been used by most institutions, organizations, schools and colleges for conducting exams. The most commonly used online examination system is conducted by giving user id and password for candidates and then logging into the current web page and answering the questions. It has lot of bugs and anyone can misuse the password and anyone can malpractice in the exam. Thus a need of secure system is required. In this project we use an enhanced model raspberry pi 3. Also we use webcam for capturing the image which captures the image when it detects any motion by using the Passive Infrared Sensor (PIR)and the captured image is sent to the raspberry pi for face detection with the help of openCV. Then, the face detected is compared with the database, to check whether face detected is applied candidate or not , if it matches then webpage on which the questions are available is opened and the candidate can continue with the exam. Thus, it provides a secured online examination system
Nowadays, the online examination has become a growing trend in education assessment. It has been adopted by various institutions, colleges and schools to be effectively conduct exam. An online examination without any authentication is like unto a programmer without any knowledge about the coding. There are various techniques used for authentication. Even though there are different constraints of online platform and surrounding environment, but they cannot be entirely relied upon. The traditional username-password is one such mean as this. But the traditional system has many loopholes as the student can share his or her’s passwords with other and can do malpractices. Hence to prevent such things we go for a more sophisticated method of authentication by using face detection In our project we have done both face detection and face recognition using raspberry pi 3 model which is a minicomputer of a credit card size and also by using webcam. For the real time image processing we have used Open Source computer vision (Open CV) which is a widely available and advantageous image processing software tool. In the Advanced Online Examination using Raspberry Pi we have used a PIR sensor to detect the motion of a person turns on the webcam and we have also used a system which can detect as well as recognize a person. If the person has been recognized the webpage of the exam is opened and he can attend the exam and the questions are displayed from the database.
The Secure Online exams using students devices makes use of the Learning Management System (LMS) such as the Moodle to perform exams. The examination is performed on student’s laptops. However the student authentication is not done by using this system. The secure online exams on thin client uses the Moodle to manage the quiz activity. Ubuntu OS is fast, free and incredibly easy to use. The LTSP adds thin client support to Linux servers.
In this system we are providing an integrated system which provides both face detection as well as face detection of the person who appears for the examination. The proposed system uses provides the security for writing the examination. The system uses the PIR sensor to detect the motion of a person and makes the webcam to be switched ON. The webcam is connected to the cam port of the Raspberry Pi 3 model. The Raspberry Pi 3 is an advanced model compared to other Raspberry Pi models with 1.2 GHz quad core and wih 1GB RAM. The image is captured and with the use of haar cascade classifier the face is detected and is compared with the database to recognize it. If the face is recognized then the webpage of the exam is opened and the questions are displayed.
- Raspberry Pi
- PIR sensor
This advanced online examination system can thus be created to provide both face detection and face recognition. It is a compact system and also a cost effective system which uses the Haar cascade classifier for face detection with an accuracy of 92.5%. The connection of the system to the laptop can be done by wireless connection whereas in Raspberry Pi 2 does this by an Ethernet connection.
The system can also be used for various other applications such as for security in houses, banks,etc. The system can provide a more efficient, compact and a less cost system that can provide both face detection and recognition.