Detecting Impersonators in Examination Centres using AI

 

Detecting impersonators in examination halls is important to provide a better way of examination handling system which can help in reducing malpractices happening in examination centers.  According to the latest news reports, 56 JEE candidates who are potential impersonators were detected by a national testing agency. In order to solve this problem, an effective method is required with less manpower. With the advancement of machine learning and AI technology, it is easy to solve this problem. In this project we are developing an AI system where images of students are collected with names and hall ticket numbers are pre-trained using the KDTree algorithm and the model is saved. Whenever a student enters the classroom, the student should look at the camera and enter class, after the given time or class is filled with student’s information will store in a  video file with student name and hall ticket no . The video will have a user with hall ticket no and name on each face. If admin finds any unknown user tag on face admin can recheck and trace impersonators . 

Problem statement:

Detecting impersonators in examination halls is important to provide a better way of examination handling system which can help in reducing malpractices happening in examination centers.  According to the latest news reports, 56 JEE candidates who are potential impersonators were detected by a national testing agency.

Existing system:

                  Information given in the hall ticket is used as verification to check if the student is the impersonator or not.  Manual security checks are performed with are not perfect and sometimes students can even change images from the hall ticket.    

Advantages:

           Manual verification methods are used for checking personally for each student which is not possible to check each student personally.

             Chances of changing images from hall tickets are possible which doesn’t have a verification method.

Proposed system:

  • In the proposed system initially, images of each student are collected and each dataset consists of 50 images of each student. These images are trained using kdtree algorithm using the image processing technique and the model is saved in the system this model can be used for automatic prediction of students in exam halls from live video or images. 

Advantages:

  • The student verification process is fast and accurate with the least effort. Reduces impersonator’s issue with live verification.
  • Time taken for prediction and processing is less and prediction done automatically using a trained model.
  • A trained model can be used to track live video and automates the process of detecting students at exam centers and display in the video.  

SOFTWARE REQUIREMENT: 

  •  Operating system:           Windows XP/7/10
  • Coding Language:           python

  • Development Kit             anaconda

  • Library:     Keras, OpenCV

  • Dataset:   any student’s dataset

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