1. FACE MASK DETECTION AND TEMPERATURE
MEASUREMENT SYSTEM
Kaushik Dahiwale
Tejas Mahajan
Pratik Dhere
Yash Pednekar
Project Guide :
Dr. Sameer Nagtilak Sir

2. Introduction:
● Overcome the Risk of Spread Of COVID-19.
● Many affected people showed no symptoms.
● Slow down the transmission of COVID-19.
● Notable symptom of COVID-19 is high body temperature.

3. Problem
Statement
The large-scale losses that have been noticed across
the world due to the covid-19 pandemic have been
highly shocking and led to a lot of loss of property and
life. The pandemic was sudden and the people and
governments could not prepare themselves
effectively beforehand to mitigate the effects of this
epidemic. This virus is highly deadly and has caused
multiple casualties which could be prevented through
effective preventive measures. Therefore, masks
enable effective prevention and further spread of the
virus, which can be the main ingredient for stopping
the infections in their path.

4. Present Theories:
Projects with similar intent have been quite popular due to the ongoing pandemic.
In the paper by Adnane Ca-bani and his colleagues from Universite de Haute-Alsace-
a method was proposed to utilize haar-cascade-based feature detectors to
individually determine the presence of noise and mouth from a detected face. Their
logic follows that no mask is worn if we can successfully detect a mouth from the
face, a mask is worn incorrectly if we can detect a nose by not a mouth, and a mask
is worn correctly if we can detect neither a nose nor a mouth. This approach is
efficient and intuitive but has severe limitations - it can only process full-frontal
faces and one can easily trick the detector by covering their mouth and nose with
their hand.

5. Proposed Methodology
● This project mainly aims to implement face mask detection and recognition of faces without masks.
● It has a non-contact temperature sensor to read the body temperature and allows the person if they
clear the COVID-19 protocols.
● The implementation is done by using Raspberry Pi with a camera for capturing images and python
● .Keras and TensorFlow are used for implementing the Machine Learning algorithm.

6. Hardware
System works as follows:
1. A camera is connected to the Raspberry Pi will
stream live video or Captures Images.
2. Faces of people should be detected by Python
Script using OpenCV, and Process them based
on the Script. This is done by implementing a
detection algorithm on the Raspberry Pi.
3. By using the sample images we train the system
by providing image datasets with mask and
without a mask
4. We apply each image to this trained classifier
this algorithm compares the input image with
the trained dataset and we get results on the
screen.
5. If the face mask is detected then it can be
shown by green mark otherwise a red mark is
displayed.

7. Specifications
● Broadcom BCM2711, Quad core
Cortex-A72 (ARM v8) 64-bit SoC @
1.5GHz
● 2 USB 3.0 ports; 2 USB 2.0 ports.
● Operating temperature: 0 – 50
degrees C
● 2-lane MIPI DSI display port
● 2-lane MIPI CSI camera port
● 5-megapixel model was released in
2013
● 8-megapixel Camera Module v2 was
released in 2016
● 12-megapixel High Quality Camera was
released in 2020
● But as considering the current situation
only 5mp camera is available.
Raspberry Pi

8. Temperature Sensor :
● Range : -40 to 85°C (ambient temperature)
-70 to 382.2°C ( object temperature)
● Time : Takes less than a second to give its
● Output
● View : 90° Field of view
● Versions : 5V version: 4.5 to 5.5V power
3V version: 2.6 to 3.6V power
LED’s :
● Voltage Range : Between 1.8 and
3.3 volts
● Pins : RGB have 4 pins .
One for each colour and one common pin
which is anode and others are cathode

9. Mask System
● Fresh install of the Raspberry Pi OS,takes at least 5 hours
● Install Keras and TensorFlow (libraries used for project)
● Build the system with various photos using machine learning
(Python)
● Giving front and sideways images of human using masks and
not using masks which will be easy for detection

10. Future Scope :
● In future work, we will propose a deep learning
framework to monitor the physical interaction
(social distancing) between individuals in a real-
time environment as a precautionary step
against the spread of the COVID-19.
● We will also focus on the detection and
classification of transparent face mask types.
● According to the WHO guidelines, sneezing and
coughing are the major symptoms of COVID-19,
in the future, we will work on analyzing the
individuals who are coughing and sneezing by
using the deep learning models, which will be
helpful in controlling the spread of COVID-19.

11. Future Scope :
● If we have to build this project on a large
scale we will Jetson by Nvidia.
● Jetson contains graphics card which is easy
for large scans and faster output.
● Nvidia kit is high in price that's the reason
many people avoid it

12. Summary : In this work, an IoT-enabled System that, monitors body
temperature and detects face masks can enhance public
safety. This will help to reduce manpower while also
providing an extra layer of protection against the spread of
Covid-19 infection. The model uses a real-time deep learning
system using Raspberry pi to detect face masks and
temperature detection. Future development will include
improving the accuracy of these steps, using a combination
of various features, and improving performance, As a result,
authorities will be able to take immediate action following
pandemic safety standards.

13. Project
Status

14. Hardware :
On hardware we have bought Raspberry pi,and other required equipments.
Software :
We first developed the logic using image processing in MATLAB but we understood that AI and ML can be
easily done in python due to its various libraries and easy coding environment.

15. Current
Status

16. ● Talking about current status we are done with our project.
● We have completed 100% of our project with 98% accuracy,where the red box shows no mask detected
whereas green box shows mask detected.
● If the temperature is more than 40 degree red LED will blink showing the danger.

17. ● We detect temperature both in celsius as well as in Fahrenheit for the clear understanding
● If the temperature exceeds more than 39 degree red LED will blink showing the danger.

18. Internship Report

19. 1 2
3 4
Company Details:
Kaushik Dahiwale - Wipro Tejas Mahajan - Eduskills Foundation
Pratik Dhere - Eduskills Foundation Yash Pednekar - Renutron

20. Wipro Internship :
● My joining was done on 26 February 2022
● Completed my 10 weeks internship in Wipro .
● We had faced three assessments which was based on project,theory knowledge.
● After assessment we had done project on Automation of Travel Agency followed
by the Certificate of Completion.
Kaushik Dahiwale

21. Renutron Internship
● Working at Renutron PVT LTD which is known for UPS building.
● First I worked in PCB training.
● Now I am working on wiring and testing which will be followed by assessments.
Yash Pednekar

22. Thank You….
Wear your masks and
Be Safe