The document describes the Visage Radar project being developed by students at SREEPATHY INSTITUTE OF MANAGEMENT AND TECHNOLOGY. The project aims to design a prototype that can recognize if a person is wearing a face mask, take contactless temperature readings, and control automated entry/exit. It will include modules for input, processing, and output. The team has conducted initial research and developed a basic model for mask recognition using CNN and TensorFlow. Their next steps are to improve mask and social distancing detection, integrate hardware, and develop an IoT/remote monitoring system to provide real-time data on safety compliance.

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. VISAGE Radar includes following modules:-
● Input module
● Microprocessor module
● Output module
Proposed Solution - VISAGE Radar
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IR PAIR

7. 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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8. ● 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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9. ● 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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10. ● 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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11. 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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12. Face mask recognition in real time
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13. ● 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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14. ● Provide a public shield into public.
● Provide awareness among uneducated people and old peoples
● Reduction in pandemic spread.
CONCLUSION
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15. 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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