Authors :
Devesh Bedmutha; Purva Bapecha; Digambar Chaure; Piyush Bora; Rachna Karnavat
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/mrw3c65k
Scribd :
http://tinyurl.com/2p9b4673
DOI :
https://doi.org/10.5281/zenodo.10464663
Abstract :
An AI based Online Proctoring System isn’t a
new concept and many such capable exam portals do
already exist. However, all of them have an unsolved design
flaw which is server side processing. To detect any
suspicious activity, the sites either take a snapshot of the
examinee in regular intervals which is doable but is very
weak, or they continuously send the video feed over to
the server for processing which being comparatively more
effective, is highly expensive. Sending video feeds of tens
of thousands of students and processing them in real
time can be very heavy on the server as well as costly for
the client. To counter all these flaws, proposing an AI
based proctoring system that securely works on the client
side. Overall goal is to allow the face detection system and
suspicious activity detection system to run on the client
side which will significantly reduce the server load and
dependency on the network. In this review paper we
explored various algorithms for face verification, object
detection, also reviewed pre-existing OPS systems and
learned about their architecture.
Keywords :
CNN (Convolution Neural Network), OPS (on- line proctoring system), TFOD (Tensorflow Object Detection).
An AI based Online Proctoring System isn’t a
new concept and many such capable exam portals do
already exist. However, all of them have an unsolved design
flaw which is server side processing. To detect any
suspicious activity, the sites either take a snapshot of the
examinee in regular intervals which is doable but is very
weak, or they continuously send the video feed over to
the server for processing which being comparatively more
effective, is highly expensive. Sending video feeds of tens
of thousands of students and processing them in real
time can be very heavy on the server as well as costly for
the client. To counter all these flaws, proposing an AI
based proctoring system that securely works on the client
side. Overall goal is to allow the face detection system and
suspicious activity detection system to run on the client
side which will significantly reduce the server load and
dependency on the network. In this review paper we
explored various algorithms for face verification, object
detection, also reviewed pre-existing OPS systems and
learned about their architecture.
Keywords :
CNN (Convolution Neural Network), OPS (on- line proctoring system), TFOD (Tensorflow Object Detection).
