This document proposes using computer vision and machine learning to monitor exams for cheating. Cameras would track student behavior and feed data to a model during exams. The model analyzes posture, head/eye position, lips, hands and individual behaviors to identify potential cheating in real-time or offline. Alerts are generated and a report shows if behaviors broke rules. The solution aims to address issues like impersonation and mass cheating due to lack of proper monitoring in remote areas and few invigilators.

1. Examination monitoring and proctoring
using
Advanced computer vision
And
pose estimation
Using advanced computer vision for identity verification while student is entering the
exam hall and then tracking the student behaviour till the end of the exam. By
collecting the data of student behaviour the machine learning algorithm will flag if the
student is not following the exam rules.

3. Due to insufficient number of
invigilators in remote areas and no
proper administration there is lack of
exam monitoring.
Numerous cases of cheating by
impersonation have been reported.
Moreover the exam centers are not
monitored properly and incidents like
mass cheating happens.
Problem Statement

4. Lack of administration by educational institutions/authorities
Problem Factors
1
2 Lot of political influence and corruption
3 Insufficient number of invigilators to ensure fair examinations
4
In remote areas due to lack of infrastructure, the exams are conducted at a
district centre. As a result, students especially females miss out on them.
5 Lack of accountability and enthusiasm displayed by the teachers

5. Using facial recognition we will verify the person taking the exam is the
same person who had registered. This verification takes place throughout
the exam and a graphical representation of their presence is shown on a
dashboard.
Solution

6. Solution
Using machine learning model to analyze student behaviour.
The model will take multiple parameters into consideration.
● Position of paper and answer sheet
● Position of student
● Body posture of student
● Head orientation of student
● Lips landmark of students
● Hand movement
● Individual student’s behavior in the past
Using all these details the machine learning model will be
able to identify and raises alert in real time.
This solution also will also work offline so even in remote
areas where there is lack of connectivity it can be
implemented.

7. Student face is scanned and
verified with picture on ID card
Student settles in allotted seat
Students behaviour and interaction
with others is monitored.
Normal Alert
Student Behavior Report
Solution Proposed

8. Behavioral plot

9. Behind the scenes
Body Posture Data
Head Orientation
Hand Monitoring
Deep Neural
Network Classifier
Score - How likely he is
breaking exam rules
Lips and Eye
tracking

10. Infrastructure for implementation
(simplicity is greatness)
High Definition Web
cameras in all classroom.
This will collect the live
feed for processing.
If school doesn't have
infrastructure to process
the data in real time. The
data can be sent to
centralized repository
where data can be
processed and actions
can be taken in case if any
cheating.
HD Cameras Storage Drive
Computers to process the
live feed data in real time.
If school has this
infrastructure. Alerts can
be generated in real time.
Computers

11. Business Model

12. Future Scope
● Encryption of videos to ensure privacy and prevent tampering
● Include more behavioral analysis like gaze estimation
● Analysing video feed from array of cameras
● Expanding the platform to a larger scale

13. Team
The Rebooters
Pranav Bijapur
Backend Developer
Sabre Travel Technologies
Shrenik Parakh
Software Development
Engineer In Test
McAfee
Sukanya Patra
Data Engineer
McKinsey & Company
Rahul Kumar
Data Engineer
McKinsey & Company