Major Project Report
on
Face Mask Detection Using Convolutional
Neural Network
Submitted By
Chandresh Singh (201800624)
Parag Ghosh (201800639)
In partial fulfilment of requirements for the award of degree in
Bachelor of Technology in Computer Science and Engineering
(2022)
Under the Project Guidance of
External Guide
Mr. Ashis Pradhan
Assistant Professor (SG). Dept. of CSE
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
SIKKIM MANIPAL INSTITUTE OF TECHNOLOGY
(A constituent college of Sikkim Manipal University)
MAJITAR, RANGPO, EAST SIKKIM – 737136
Project Completion Certificate
This is to certify that the below mentioned student(s) of Sikkim Manipal Institute of
Technology have worked under my supervision and guidance from 10 January 2022
to 31 May 2022 and have successfully completed the project entitled “Face Mask
Detection Using Convolutional Neural Network” in partial fulfilment of the
requirements for the award of Bachelor of Technology in Computer Science and
Engineering.
University Registration No.
Name of Student
Course
201800624
Chandresh Singh
B.Tech (CSE)
201800639
Parag Ghosh
B.Tech (CSE)
Mr. Ashis Pradhan,
Associate Professor (SG),
Sikkim Manipal Institute of Technology,
Majitar, East Sikkim -- 737136
I
Project Review Certificate
This is to certify that the work recorded in this project report entitled “Face Mask
Detection using Convolutional Neural Network” has been carried out by Mr.
Chandresh Singh (201800624) and Mr. Parag Ghosh (201800639) of Computer
Science & Engineering Department of Sikkim Manipal Institute of Technology in
partial fulfilment of the requirements for the award of Bachelor of Technology in
Computer Science and Engineering. This report has been duly reviewed by the
undersigned and recommended for final submission for Major Project
Mr. Ashis Pradhan
Associate Professor (SG),
Department of Computer Science & Engineering
Sikkim Manipal Institute of Technology
Majitar, East Sikkim – 737136.
II
0.
Certificate of Acceptance
This is to certify that the below mentioned students of Computer Science &
Engineering Department of Sikkim Manipal Institute of Technology (SMIT) have
worked under the supervision of Mr. Ashis Pradhan, Associate professor(Selection
Grade) from 10th January 2022 to 10th June 2022 on the project entitled “Face
Mask Detection using Convolutional Neural Network”.
The project is hereby accepted by the Department of Computer Science &
Engineering, SMIT in partial fulfilment of the requirements for the award of Bachelor
of Technology in Computer Science and Engineering.
University Registration No Name of Student(s)
Project Venue
201800624
Sikkim Manipal Institute of
Chandresh Singh
Technology
201800639
Parag Ghosh
Sikkim Manipal Institute of
Technology
Dr. Kalpana Sharma
Professor & HOD
Computer Science & Engineering Department
Sikkim Manipal Institute of Technology
Majitar, Sikkim - 737136
III
Declaration
We the undersigned, hereby declare that the work recorded in this project report
entitled “Face Mask Detection using Convolutional Neural Network” in partial
fulfilment for the requirements of award of B.Tech in Computer Science &
Engineering from Sikkim Manipal Institute of Technology (A constituent college of
Sikkim Manipal University) is a faithful and bonafide project work carried out at
Sikkim Manipal Institute of Technology under the supervision and guidance of Mr.
Ashis Pradhan , Associate Professor(SG).
The results of this investigation reported in this project have so far not been reported
for any other Degree / Diploma or any other Technical forum.
The assistance and help received during the course of the investigation have been duly
acknowledged.
Chandresh Singh (201800624)
Parag Ghosh (201800639)
IV
Acknowledgement
We would like to convey my heartful thanks to our Internal guide Mr. Ashis
Pradhan, Associate Professor(SG) without whom the completion of this project
would not have been possible.
We pay deep sense of gratitude to Prof. (Dr.) Kalpana Sharma, H.O.D, Computer
Science & Engineering Department, Sikkim Manipal Institute of Technology for
giving me the opportunity to work on this project and provided all support required.
We would like to express my humble gratitude to Mr. Biswaraj Sen, Associate
Professor, Mr. Santanu Kumar Misra, Associate Professor, Mr. Saurav Paul,
Assistant Professor-I, and Mrs Chitrapriya N., Assistant Professor-I, Project
Coordinators, Computer Science and Engineering Department, Sikkim Manipal
Institute of Technology for their unlisted encouragement and their timely support and
guidance till the completion of the project work.
Chandresh Singh (201800624)
Parag Ghosh (201800639)
DOCUMENT CONTROL SHEET
V
DOCUMENT CONTROL SHEET
CSE/Major Project/B.Tech/In-House/Group-
1
Report No
2
Title of the Report
Face Mask Detector
3
Type of Report
Technical
4
Author(s)
1 /2022
Chandresh Singh
Parag Ghosh
5
Organizing Unit
Sikkim Manipal Institute of Technology
6
Language of the Document
English
The project aims to develop a model
that”can accurately detect masks over the
7
Abstract
face in “public areas”(such as “airports,
railway stations”, crowded marketplaces) to
reduce the spread of Coronavirus. Therefore,
contributing to public” health.
8
Security Classification
General
VI
LIST OF CONTENTS
SL NO.
TITLE
PAGE NO.
0
Abstract
1
Introduction
1–5
1.1
General Overview of the Problem
1-2
1.2
Literature survey
3-4
1.3
Problem definition
5
1.4
Software Requirement and Specification
6
1.5
Dataset
7
1.5
Solution Strategy
8
2
3
0
Design
9 -12
2.1
Flow Chart
9-10
2.2
Sequence Diagram
11
2.3
Activity Diagram
12
Implementation
13-22
3.1
Keras
13
3.2
Tensorflow
13
3.3
Pseudo code for Phase:1
14
3.4
Pseudo code for Phase:2
15
3.5
Pseudo code for Phase:3
15
3.6
Scene Builder
17
3.7
Video Capturing
18
3.8
Mask using CNN
20-21
3.9
Accuracy
22
3
Results and Discussion
23-24
4
Summary And Conclusion
25
5
Limitations and Future scope
26
6
Gantt Chart
27
References
28
Plagiarism Report
VII
LIST OF FIGURES
SL
TITLE
NO.
PAGE
NO.
1
Block Diagram (phase 1,2,3)
9-10
2
Sequence Diagram
11
3
Activity Diagram
12
4
Face Detection using Haar
16
5
User Interface for Face Detection
17
6
BGR color in OpenCV
19
7
CNN model hidden layer for Face Mask detector
20
8
Formula to calculate accuracy for the model
21
9
Graph for training accuracy vs validation accuracy
21
10
Graph for training loss vs validation loss
22
11
Graph for training loss vs validation loss
22
12
Mask detection on a face with no mask results in a 100 %
23
No-mask.
13
Person wearing mask inappropriately resulting in mask
23
coverage as 66.97
14
Confusion matrix for face mask detection
24
15
Gantt Chart
28
VIII
LIST OF TABLES
TABLE
TABLE NAME
NO.
1.1
PAGE
NO.
Literature Survey
3-4
IX
ABSTRACT
With the reopening of places and businesses under lockdowns, health institutions are
suggesting Face masks as “essential measures” to keep citizen “safe when” venturing
outdoors. To mandate the use of face masks, it becomes essentials as a country’s
citizen to help “enforce individuals to apply mask before exposure to public places”.
Here HAAR-CASCADE algorithm can be used for feature detection in an image.
These” classifiers “result a high recognition” rate with range of “expressions”,
efficient” feature “selection” and “low assortment” of “false positive” features.
Cascade classifiers like “HAAR uses” only “200 features “out of 6000 features” to
yield recognition of about 85-95%.
The project aims to develop a “technique that”can accurately”detect masks over the
face in “public areas” (such as “airports, railway stations”, crowded marketplaces) to
reduce the spread of Coronavirus . Therefore, contributing to public” health.
0
1. INTRODUCTION
1.1 General Overview of the Problem
COVID-19, a novel virus, first detected in Wuhan in 2019.Which later become a
global public health issue. This disease is wreaking havoc on the world's poorer
economies. Severe Acute Respiratory Syndrome Coronavirus 2 is a once-in-a-century
respiratory viral illness (SARS-CoV-2). The pandemic is wreaking havoc on society
and economies all across the world, resulting in a global health emergency. It has
been a major healthcare concern throughout the world, particularly in the third wave.
As a result of the outbreak, several businesses have closed their doors. Furthermore,
due to their enormous impact on people's daily lives, numerous areas such as
maintenance projects and infrastructure building have not been interrupted.”
The virus had rapidly spread to most countries throughout the planet as of April
2021.According to the most recent WHO figures, there have been 152,543,452
confirmed cases and 3,198,528 deaths. Corona-virus infection is transmitted primarily
through respiratory droplets produced when people breathe, talk, cough, or sneeze,
with a common droplet size of 0.5-1.0m, according to the Centres for Disease Control
and Prevention (CDC), but aerosol emission increases when humans speak or shout
loudly.”
Because present regulatory measures are unable to prevent COVID-19 from spreading
rapidly, most world governments have proposed a variety of solutions, including
imprisonment and lockdowns.
However, in addition to the public goods game, game-theoretic scenarios can be used
to investigate COVID-19 management inefficiency. Some academics have
concentrated on governments' reluctance to impose onerous but necessary viral
containment measures (such as stay-at-home orders and lockdowns), as well as noncooperation for reasons other than free riding. For example, because tight stay-athome measures can have a significant impact on people's livelihoods, the cost of
staying at home (combined with lockdown weariness) may end up outweighing the
risk of infection from venturing out, according to authors in.”
1
When vaccinations become accessible at the end of 2020, scientists predicted that
vaccine adoption programs will outstrip other factors including vaccine efficacy and
isolation processes. Using social network analysis, and agent-based modelling, the
scientists argued that "demographics, physical location, the level of interaction, the
health of the vaccine,” epidemic parameters, and perceptions about the vaccine being
introduced" would influence people's vaccination decisions, while "epidemic
parameters, the nature of the vaccine being introduced, logistics, the management of
human resources" would influence government decisions.
To summarise, COVID-19 management necessitated an awareness of a range of
elements that calibrated payoffs and changed individual and governmental behaviours
toward safety. True, COVID-19 is a global epidemic that affects a variety of domains.
Nonetheless, it paved the way for computer science researchers. A variety of study
areas, including developing new COVID-19 automatic detection algorithms, and
recognising people wearing or not wearing masks. Using dataset from Because there
have been some flaws and delays in the early laboratory testing, researchers have
concentrated on various approaches As a result, combining modern AI techniques
with chest radiological imaging can lead to a more accurate detection of the COVID19 and aid in addressing the problem of specialised physician shortages in remote
areas.
2
1.2 Literature Survey
YEA
R
PAPER DETAILS
AUTHO
R
FINDING
PROJECT
RELEVAN
CE
2022
A real time face mask detection Hiten
system using convolutional neural Goyal
network
Karanve
Publication Date-25/2/22
er Sidana
The model
uses CNN
as
backbone
architectur
e which
can be
implement
ed for any
image to
detect
presence
of a mask.
The Image
is then fed
to the face
detection
model first
to detect
all the
faces in a
provided
image
The Paper
also discuss
about face
mask
detection
system for
static images
to identify if
a person
picture has a
mask on or
not.
Multimedia Tools and Applications, Charanje
81(11)
et Singh
Pp:7-12
3
2017
Face Detection and Tracking Using
OpenCV
Publication Date-31/07/2019
International_Conference_on_Electr
onics, Communication_and
Aerospace Technology
Pp: 474-478
Kruti
Goyal,
Kartikey
Agarwal,
Rishi
Kumar
The paper
proved
helpful in
image
processing,
and feature
tracking of
the face
It talks
about the
Haarclassifier
Table 1.1: Literature Survey
4
The paper
was helpful
in
segmentation
of an image
for feature
extraction
using the
haar
classifier and
storing the
interested
region into
an array
1.3 Problem Definition
Government of India has 2 clear mandates for public health and security of citizens to
fight against Covid-Masks and Vaccines. The later having a coverage of 62% of
adults being vaccinated. Many counters for people not wearing masks have been
implanted including fines and even jail under section 188.People keep finding ways to
break the law when no-one is watching.
The process of keeping in check if the citizens are wearing masks or not is
particularly manually taxing in crowded places like public institutions and “private
institutions.
As very small proportion of the people wear masks most amongst them still tend to
use it not for safety rather than for symbol to show that they have it. Wearing mask by
not properly covering their mouth and face.
Referred pdf - “The later having a coverage of 62% of adults being vaccinated”
https://www.mohfw.gov.in/pdf/GuidelinesforCOVID19VaccinationofChildrenbetwee
n15to18yearsandPrecautionDosetoHCWsFLWs&60populationwithcomorbidities.pdf
5
1.4 Software Requirement and Specification
A. Hardware Specification of Developing Environment
•
Laptop/computer with minimum RAM: - 8 GB
•
Hard Disk: 360 GB or more
•
Processor: Intel Pentium i3 or above
•
Graphics Card: 2GB or more
B. Software Specification
•
OS: Windows 8 or above, Linux, Mac
•
Python (version 3.10)
•
Scene Builder (UI controller), Pycharm, Juypter Notebook
•
Libraries: OpenCV, Tensorflow, Keras, Pytorch
6
1.5 Dataset
The dataset used in this research was collected in picture formats of JPEG Figure 1
exhibits the sample of the dataset. It was a mixture of different open-source datasets
and images, including dataset for Face Mask Detection by techhoney and dataset for
PYImageSearch_reader by Prajna Bhandary. As a result, there were different varieties
of images with variations insize and resolution. All the photos were from open-source
resources, out of which some resemble real-world scenarios, and others were
artificially created to put a mask on the face. Major Facial landmarks include
the eyes, nose, chin, lips, and eyebrows. This intelligently creates a dataset by forming
a mask on a non-masked image. Finally, the dataset was divided into two classes or
labels. These were ‘with mask’ and ‘without mask’, and together, the images were
curated, aggregating to around 4000 images.
7
1.6 Solution strategy
Phase 1: Data pre-processing
1.1: Load the images from the dataset
1.2: Label images into classes ([0] for the class containing mask images, [1] for the
class containing without-mask images)
1.3: Spilt dataset into test and train (80:20 as the total images in dataset < 10,000
images)
1.4: Apply data augmentation (resize, crop, shift-to-right, shift-to-left, rotate 90
degrees)
Phase 2: CNN model training for Mask
2.1: Use ReLu as Activation function in Conv2D layer
2.2: Add Maxpool2D layer
2.3: Add flatten layer to the model
2.4: Use model. eval function to add the dropout layer in the model
2.5: Use Softmax as Activation function in the dense layer which use probability to
generate binary output.
2.6: Use model. train to train the model on training dataset.
Phase 3: Applying Mask model on face detector
3.1: Load the saved mask CNN model
3.2: Convert input image from BGR to Grayscale
3.3: Import frontal_face.xml model for face detection
3.4: Use multiscale function (returns value in an array [ x ,y ,w , h] that can be used to
draw a rectangle around the detected face).
3.5: Input the resulting array into the masked model to get binary output of 0(mask)
and 1(No-mask).
8
2. DESIGN
2.1 Flow charts depicting different phases for the model of face mask detector
Fig 1.1: Flow Chart for Phase 1
Fig 1.2: Flow Chart for phase 2
9
Fig 1.3: Flow Chart for phase 3
10
2.2 Sequence Diagram
Fig 2: Sequence Diagram
A sequence diagram is a type of interaction diagram because it describes how and in
what order a group of objects works together. These diagrams are used by software
developers and business professionals to understand requirements for a new system or
to document an existing process. Sequence diagrams are sometimes known as event
diagrams or event scenarios.
Sequence diagrams can be useful references for businesses and other organizations.
●Represent the details of a UML use case.
●Model the logic of a sophisticated procedure, function, or operation.
●See how objects and components interact with each other to complete a
process.
● Plan and understand the detailed functionality of an existing or future scenario.”
11
2.3 Activity Diagram
Fig 4: Activity Diagram
As the input is taken from the camera, frame rate and a small of delay of 1 ms is set.
A multi-scale model is used for feature extraction using haar based edge classifier to
detect face in an image. When the face is not detected, we capture the image again
and call multi-scale until a face is found. But when a face is found we import the
mask model to detect mask. Which at the end uses SoftMax layer to return a output
using probability function resulting in an output of mask and mask-less.
12
3. IMPLEMENTATION
3.1 Keras
Keras is an API designed for human beings, not machines. Keras follows best
practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes
the number of user actions required for common use cases, and it provides clear &
actionable error messages. Keras is a minimalist Python library for deep learning that
can run on top of Theano or Tensor Flow. It was developed to make implementing
deep learning models as fast and easy as possible for research and development
Keras was developed and maintained by François Chollet, a Google engineer using
four guiding principles:
 Modularity: A model can be understood as a sequence or a graph alone. All the
concerns of a deep learning model are discrete components that can be combined in
arbitrary ways.
 Minimalism: The library provides just enough to achieve an outcome, no frills and
maximizing readability.
 Extensibility : New components are intentionally easy to add and use within the
framework, intended for researchers to trial and explore new ideas.
 Python: No separate model files with custom file formats . Everything is native
Python . Keras is designed for minimalism and modularity allowing you to very
quickly define deep learning models and run them on top of a Theano or TensorFlow
backend.
3.2 Tensorflow
TensorFlow is a free and open-source software library for dataflow and differentiable
programming across a range of tasks. It is a symbolic math library, and is also used
for machine learning applications such as neural networks. It is used for both research
and production at Google, TensorFlow is Google Brain's second-generation system.
While the reference implementation runs on single devices, TensorFlow can run on
multiple CPUs and GPUs (with optional CUDA and SYCL extensions for generalpurpose computing on graphics processing units).
13
3.3 Algorithm for phase 1-: Data pre-processing
3.3.1. Load the images from dataset
Step:1 Start
Step:2 Set experiment path to the path of a newly created folder
Step:3 Set data_path to the path where dataset is stored
Step:4 Set file_path with os.join with experimental_path and data_path
Step:5 Read files from file_path
Step:6 For loop starts for file in file_path
6.1
check if file is from mask folder
6.1.1
6.1
append file to file_path with its name changed to 0
if not append file to file_path with its name changed to 1.
Step:7 End
3.3.2 Split the dataset into test and train
Step:1 Start
Step:2 Set file_name to empty
Step:3 Read files from file_path where the combined dataset is stored
Step:4 Set train_size to 0.8 with random_state being any positive number
Step:5 Set test_size to 0.2 with random_state being same to that of the train dataset.
Step:6 End
3.3.3 Apply data augmentation
Step:1 Start
Step:2 Perform resize () to make all that training images the same size
Step:3 Convert image to tensor
Step:4 Perform Normalize on the image change to the range of pixel intensity value
Step:5 Repeat step 1-4 for test images
Step: 6 Stop
14
3.4 Algorithm for phase 2-: CNN model training for Mask
3.4.1 Use model.train() to train model on training dataset.
Step:1 Start
Step:2 Set the model in train mode
Step:3 Make an array for loss[]
Step:4 For loop start for batch in train dataset
Step:5 train step(defines the training step using gradient descent)
Step:6 clear gradients by setting optimizer_grad to zero.
Step:7 backward loss to calculate gradient
Step:8 update the parameter using append loss.
Step:9 Stop
3.5 Algorithm for phase 3-: Mask model on face Detector
Step:1 Start
Step:2 Load the mask model
Step:3 Use image processing function(imgproc) to convert BGR images into
Grayscale
Step:4 Import frontal_face.xml classifier
Step:5 Find centre of the image
Step:6 Use multiscale function(to return value in matofRect[x,u,w,h].
Step:6 Use matofRect to draw a rectangle around the face
Step:7 Pass matofRect to the mask model to get probability function output of 0 and
1.
Step:8 Use value of output to print (0 for mask) and (1 for without mask)
Step:9 Stop
15
Fig 4: Face detection using Haar
3.6 Scene Builder
Fig 5: User Interface for face detection
16
The need to control the button push and the refreshment of the image view. To do so
we have to create a reference between the GUI components, and a variable used in our
controller class:
The @FXML tag means that we are linking our variable to an element of the fxml file
and the value used to declare the variable has to equal to the id set for that specific
element.
3.7 Video Capturing
All the functionalities required for video manipulation is integrated in the Video
Capture class.
In “case of a video file there is a frame rate specifying just how long is between two
frames. While for the video cameras usually there is a limit of just how many frames
they can digitalize per second. In our case we set as frame rate 33 frames per sec.”
17
BGR color order in OpenCV
OpenCV “loads the color images in reverse order and uses the BGR color
format instead” of the RGB. We can see the order of the channels in the
following diagram:
Fig 6: BGR color in OpenCV
18
Now we are going to convert our image from BGR to Grayscale format. OpenCV has
a really nice function to do this kind of transformations: “
cvtColor takes as arguments:

a source image (frame)

a destination image (frame), in which we will save the converted image.

an additional parameter that indicates what kind of transformation will be
performed. In this case we use COLOR_BGR2GRAY ( BGR default channel
order in case of colour images).”
This encodes an image into a memory buffer. The function compresses the image and
stores it in the memory buffer that is resized to fit the result.”
It takes three parameters:

(“. Png”) format for the output

(frame) Image to be written.

(buffer) Output buffer resized to fit the compressed image.
3.8 Mask Detector
Fig 7: CNN model hidden layers for Face Mask Detector
Fully Connected Layer:
It's important to add a fully connected after “convolution, non-linear, and pooling
layers functions are done. This layer receives output data from all the convolution
network layers. Fully connected layer is used at the ends of a network making an N-
19
dimensional vector, wherein N means the number of classes through which the model
chooses the needed class.”
Layers In CNN Model:

Conv2D

Flatten ()

MaxPooling2D

Dense

Dropout
1. Conv2D:
This. The Rectified Linear Unit (ReLu) function will return the data input if it really is
positive else, it will output zero.
2. Flatten ():
It's used to blend all of the levels into a single one-dimensional layer.
3. MaxPooling2D:
It is utilized with a 2*2 pool or filter size.
4. Dense:
Soft max is the activation function used here, and it produces a vector with two
probability distribution values.
5. Dropout:
It's utilised to keep the model from fitting too tightly, or in other words, from
overfitting.
20
3.9 Accuracy
It tells about the performance or how the model is performing in general
Quite useful when all the classes are of the equal importance
Calculate in the ratio between the number of correct predictions to the total number
of” predictions
Fig:8-Formula to calculate accuracy of the model
In this model the accuracy is calculated using Scikit-learn, dividing the sum of true
positives and true negatives Over all the values of confusion matrix.
Fig 9: “Graph for training accuracy vs validation accuracy
Fig 10: Graph for Training loss vs Validation loss
21
3.10 Confusion Matrix
Fig 11: Confusion matrix for Face Mask detector
Formulas used -

Precision=Tp/(Tp+Fp)=2709/(2709+81)=0.97

Recall=Tp/(Tp+Fn)=2709/(2709+135)=0.95

Accuracy=Tp+Tn/(Tp+Tn+Fp+Fn) =2709+903/(2709+903+81+135)=0.94

Misclssification=Fp+Fn/(Tp+Tn+Fp+Fn)
=81+135/(2709+903+81+135)=0.056

Specificity=Tn/(Tn+Fp)=903/(903+81)=0.92
Source-https://towardsdatascience.com/taking-the-confusion-out-of-confusionmatrices-c1ce054b3d3e
22
5. RESULTS AND DISCUSSION
Fig 12: Mask detection on a face with no mask results in a 100 %No-mask.
Fig 13: Person wearing mask inappropriately resulting in mask coverage as 66.97%.
23
Fig 14: Person wearing mask appropriately resulting in mask coverage as 100.00%.
24
4. SUMMARY AND CONCLUSION
4.1 Summary
The proposed solution for the problem definition is developed and the test cases have
been tested and the applications function properly. In this work, a deep learning-based
approach for detecting masks over faces in public places to curtail the community
spread of Coronavirus is presented. The proposed technique efficiently handles
occlusions in dense situations by making use of an ensemble of single and two-stage
detectors at the pre-processing level. The ensemble approach not only helps in
achieving high accuracy but also improves detection speed considerably.
Furthermore, the application of transfer learning on pre-trained models with extensive
experimentation over an unbiased dataset resulted in a highly robust and low-cost
system. The identity detection of faces, violating the mask norms further, increases
the utility of the system for public benefits.
25
5. LIMITATIONS AND FUTURE SCOPE
5.1 Limitation of the project
The current limitations of the project are as follows:

Less effective against varying angle of the face in the camera, as most data set
available that are used in training the model are front facing with clear feature
demarcation, this would prove to be hard to determine if a person is wearing a
mask or not.”

Lack of features-> as the input image or data does not have proper facial
features or the features are altered like eyes are covered with sunglasses, in
these types of cases, the model would not be able to tell between masked or
mask less.”
5.2 Future Scope of the project
The future scope of the Face Mask Detector:
● Model can be integrated with any high-resolution video surveillance devices
and not limit the to mask detection.
● The model can be extended to detect facial landmarks with a facemask for
biometric purposes.
26
6. GANTT CHART
Time Frame
Activity
January February March
(2022)
(2022) (2022)
Literature Survey
Problem Definition
Design
Implementation
Testing and Analysis
Documentation
LEGEND
Proposed Activity
Activity Achieved
Fig 15: Gantt chart
27
April
(2022)
May
(2022)
June
(2022)
REFERENCES
[1]
M. R. Bhuiyan, S. A. Khushbu and M. S. Islam, "A Deep Learning Based
Assistive System to Classify COVID-19 Face Mask for Human Safety with
YOLOv3," 2020 11th International Conference on Computing, Communication and
Networking Technologies (ICCCNT) Accessed:March 23,2022
[2]
M. M. Rahman, M. M. H. Manik, M. M. Islam, Mahmud and J. -H. Kim, "An
Automated System to Limit COVID-19 Using Facial Mask Detection in Smart City
Network,"
2020
IEEE
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