The document describes a project to develop a prototype radar system called VISAGE Radar that can detect whether a person is wearing a face mask, measure body temperature contactlessly, and control automated entry and exit. It will use a Raspberry Pi microprocessor along with input modules like a camera and temperature sensor. The system aims to help reduce the spread of COVID-19 in large organizations and institutions by monitoring safety protocols automatically without human intervention. The team has conducted some initial work including a proof of concept using neural networks to recognize masked and unmasked faces in real-time video feeds. Further work to integrate additional features like identifying specific mask types and social distancing is still needed.

1. SREEPATHY INSTITUTE OF MANAGEMENT AND TECHNOLOGY
KOOTTANAD PALAKKAD
Mentor details:- SEBIN SUNNY P
Asst. prof in EEE
sebinsunny@simat.ac.in
VISAGE RADAR
Team members:-
ARSHIDA P
ATHIRA p
UMA PARVATHI M M
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2. CONTENTS
1. Objectives
2. Problem definition
3. Existing solution
4. Proposed Solution & Block diagram
5. Methodology
6. Proposed product over market product
7. Innovativeness
8. Current work
9. Work to be done
10. Conclusion
11. Reference
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3. To design and develop a prototype with the following capabilities:
● Face mask recognition
● Contactless temperature sensing
○ IR SENSOR - cost effective
○ Thermal camera - Accurate & long distance
● An automated entry, exist control mechanism
To design and develop a web/app interface to get real time
monitoring of movement of people
OBJECTIVEs
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4. Problem definition
1. How can we reduce the spread of COVID 19 pandemic?
● by wearing mask
● by maintaining social distance
● by sanitizing
1. Currently face mask and temperature monitoring is done
manually. Human monitoring becomes tedious and risky in
the case of large organization and institutions.
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5. ● There is no dedicated devices available in the market to
detect facemask and temperature without human intervention.
● Following are the existing devices available in the market :
○ Contactless thermometer - operated by humans.
Existing Solutions
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6. Block Diagram
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7. Different Modules
VISAGE Radar includes following modules:-
● Input module
● Microprocessor module
● Output module
⚫ Input modules:- camera , temperature sensor, IR pair
⚫ Microprocessor module :- Raspberry pi 4
⚫ Output module :-display which denotes the entry
granted or entry denied , count display
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8. Virtual Queue Management Flowchart
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9. Methodology
● Completed a basic literature survey and selected CNN and
Tensorflow for basic mask recognition.
● A thorough literature survey need to be carried out to
identify the best and most efficient method to identify
different types of masks and checking social distancing.
● Gathering different datasets to train and validate our
solution.
● Set up a standalone system using Raspberry Pi 4 and
related hardware.
● Build a suitable contactless temperature sensing
mechanism.
● Build an automated entry/exit control mechanism which
can be incorporated with the Raspberry Pi
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10. Virtual queue management
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• The proposed system uses real time tokens over the cloud
• servers for managing the queues. The users can
conveniently use the system with an android based
application
• which allows a registered user to create or join a queue.
• The whole system is centralized and hence users can have
freedom to access the application from any android device
at any location.
• Online payment is also done
• Cloud computing is the back bone

11. ● Our device is fully automatic.
● Face mask detection and temperature sensing without
physical contact.
● Automatic entry to the building by assuring the safety.
● Records the number of people who have entered and a real
time data is provided.
● The authority can monitor the real time data remotely.
● More accurate.
● Expandable memory.
Proposed product over market product
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12. ● System detects whether a person is masked or not and also
measures body temperature.
● It is highly accurate and less expensive.
● IR sensor and thermal camera for temperature detection
Benefits of camera:-
1.Low light scenarios
2.Immune to visual limitations
3.Camouflaging Foliage
4.Fewer false Alarms
INNOVATIVENESS
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13. ● we developed a small experiment using CNN and Google
colab for detecting face mask
● gathered all the set of data
● Trained the neural network model
● Predicted the labels for Masked and Unmasked images
using the trained model.
● Developed real time monitoring system for both face
mask and temperature detection
CURRENT WORK DONE
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14. CURRENT WORK DETAILS
http://localhost:8888/notebooks/Mask%20Detection%20Project/Ma
skTest.ipynb - mask image
http://localhost:8888/notebooks/Mask%20Detection%20Project/Ma
skTest2.ipynb - without mask image
http://localhost:8888/notebooks/Mask%20Detection%20Project/Ma
sknet%20Webcam.ipynb - real time
Initial work prototype video:-https://youtu.be/8p1dol6Y6BM
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15. Face mask recognition in real time
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16. ● We are planning to implement this system as a stand alone
device which can be made available in the market.
● To integrate IoT and remote monitoring features to our
system.
● To integrate program codes for identifying different type of
mask and detecting social distance.
WORK TO BE DONE
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17. ● Provide a public shield into public.
● Provide awareness among uneducated people and old peoples
● Reduction in pandemic spread.
CONCLUSION
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18. REFERENCES
[1] Roomi, Mansoor, Beham, M.Parisa, “A Review Of Face Recognition Methods,”
in International Journal of Pattern Recognition and Artificial Intelligence, 2013,
27(04), p.1356005
[2] A. S. Syed navaz, t. Devi sri, Pratap mazumder, “ Face
recognition using principal component analysis and neural
network,” in International Journal of Computer Networking,
Wireless and Mobile Communications (IJCNWMC), vol. 3, pp.
245-256, Mar. 2013.
[3] Turk, Matthew A., and Alex P. Pentland. "Face recognition using
eigenfaces," in IEEE Computer Society Conference on Computer
Vision and Pattern Recognition, 1991, pp. 586-591.
[4] H. Li, Z. Lin, X. Shen, J. Brandith, and G. Hua, “A convolutional
neural network cascade for face detection,” in IEEE CVPR,
2015, pp.5325-5334.
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