The document discusses creating a face mask detection system using convolutional neural networks. The system aims to automatically detect faces and determine if a mask is being worn to help monitor public areas. It will use a CNN model trained on images of people with and without masks. The CNN model will learn visual patterns to detect masks without needing pre-programmed filters. The system is meant to reduce the manual effort of checking crowds and allow entry only to those wearing masks.

1. Face Mask Detection
Using Convolution
Neural Networks

2. TEAM
MEMBERS:
 Aswin Subramani
 Johanna Sharon G
 Nishanth G
 Manjusa D

3. INTRODUCTION
 "Prevention is better than cure" is one of the
effective measures to prevent the spreading
of COVID-19 and to protect mankind. Many
researchers and doctors are working on
medication and vaccination for corona.
 COVID-19 spreads mostly by droplet
infection when people cough or if we touch
someone who is ill and then to our face (i.e
rubbing eyes or nose). Ongoing pandemic
shows that it is much more contagious and
spreads fast. Depending on the infection
spreading, we have two cases: Fast and Slow
spread.

4. • A FAST PANDEMIC WILL BE TERRIBLE AND WILL
COST MANY LIVES. IT OCCURS DUE TO A RAPID
RATE OF INFECTION BECAUSE THERE ARE
NO COUNTERMEASURES TO SLOW IT DOWN.
THIS IS BECAUSE, IF THE NUMBERS OF INFECTED
PEOPLE GET TOO LARGE, HEALTHCARE SYSTEMS
BECOME UNABLE TO HANDLE IT. WE WILL LACK
RESOURCES SUCH AS MEDICAL STAFF OR
EQUIPMENT LIKE A VENTILATOR.

5. • To avoid the above situation, we need to do what we can to turn
this into a slow pandemic. A pandemic can be slowed down only
by the right responses, mainly in the early phase. In this phase,
everyone who is sick can get treatment and there is no emergency
point with flooded hospitals.
• In this pandemic, we need to engineer our behavior as a vaccine.
that is, "Not getting infected" and "Not infecting others". The best
thing we can do is to wash our hands with soap or a hand
sanitizer. The next best thing is social distancing.
• To avoid getting infected or spreading it, It is essential to wear a
face mask while going out from home especially to public places
such as markets or hospitals.

6. AIM
• To create a face mask detector to avoid the trouble of
manually checking a crowd for masks

7. WHAT IS CNN?
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial
neural network, most commonly applied to analyze visual imagery. They are also known as shift
invariant or space invariant artificial neural networks (SIANN), based on the shared-weight
architecture of the convolution kernels or filters that slide along input features and provide
translation equivariant responses known as feature maps. Counter-intuitively, most convolutional
neural networks are only equivariant, as opposed to invariant, to translation. They have
applications in image and video recognition, recommender systems, image classification, image
segmentation, medical image analysis, natural language processing, brain-computer interfaces,
and financial time series.
CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean
fully connected networks, that is, each neuron in one layer is connected to all neurons in the
next layer. The "full connectivity" of these networks make them prone to overfitting data. Typical
ways of regularization, or preventing overfitting, include: penalizing parameters during training
(such as weight decay) or trimming connectivity (skipped connections, dropout, etc.) CNNs take
a different approach towards regularization: they take advantage of the hierarchical pattern in
data and assemble patterns of increasing complexity using smaller and simpler patterns
embossed in their filters. Therefore, on a scale of connectivity and complexity, CNNs are on the
lower extreme.

8.  Convolutional networks were inspire by biological
processes in that the connectivity pattern
between neurons resembles the organization of the
animal visual cortex. Individual cortical
neurons respond to stimuli only in a restricted region
of the visual field known as the receptive field. The
receptive fields of different neurons partially overlap
such that they cover the entire visual field.
 CNNs use relatively little pre-processing compared to
other image classification algorithms. This means that
the network learns to optimize the filters through
automated learning, whereas in traditional algorithms
these filters are hand-engineered. This independence
from prior knowledge and human intervention in
feature extraction is a major advantage.

9. About The Project
 The system is designed to detect the faces and to
determine whether the person wears a face mask or
not. Using the above data, we can decide whether the
concerned person can be allowed inside public places
such as the market, or a hospital. This project can be
used in the hospital, market, bus terminals, restaurants,
and other public gatherings where the monitoring has
to be done.
 This project consists of a camera that will capture the
image of the people entering public places and detect
whether the person wears a face mask or not using
their facial features.

10. Methodology
Step 1:
 We will create a neural network model with TensorFlow and will train it on
a dataset of both people who are wearing facemasks and people who are
not.
 The algorithm will run on Jupyter notebooks but requires a lot of GPU
power to train the model. However, if we execute the code without
changing the Model settings, we can guarantee total confidence of 98%.
Step 2:
 Here, we will create a face recognition algorithm that will be able to detect
facemasks on people's faces using the trained model in the previous step.

11. THANK YOU