This document proposes an Android-based face mask detection system using convolutional neural networks (CNNs). It addresses the limitations of existing face recognition technologies during the COVID-19 pandemic. Three masked face datasets are introduced: the Masked Face Detection Dataset for detecting masked faces, and the Real-world and Simulated Masked Face Recognition Datasets for recognizing masked faces. A CNN model trained on these datasets is used to classify faces as with or without masks for applications like access control. The system aims to improve security and enable real-time face recognition during the pandemic.

1. Android Based Face
Mask Detection System
APU KUMAR GIRI
4TH SEMESTER
MASTER OF COMPUTER APPLICATION
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2. Abstract
In order to effectively prevent the spread of COVID-19 virus, almost everyone wears
a mask during coronavirus epidemic. This almost makes conventional facial
recognition technology ineffective in many cases, such as community access control,
face access control, facial attendance, facial security checks at train stations, etc.
Therefore, it is very urgent to improve the recognition performance of the existing
face recognition technology on the masked faces. Most current advanced face
recognition approaches are designed based on deep learning, which depend on a
large number of face samples. However, at present, there are no publicly available
masked face recognition datasets. To this end, this work proposes three types of
masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world
Masked Face Recognition Dataset (RMFRD) and Simulated Masked Face Recognition
Dataset (SMFRD). Among them, to the best of our knowledge, RMFRD is currently the
world's largest real-world masked face dataset. These datasets are freely available
to industry and academia, based on which various applications on masked faces can
be developed
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3. Introduction
Face recognition is a promising area of applied computer vision [1]. This
technique is used to recognize a face or identify a person automatically from
given images. In our daily life activates like, in a passport checking, smart
door, access control, voter verification, criminal investigation, and many other
purposes face recognition is widely used to authenticate a person correctly and
automatically. Face recognition has gained much attention as a unique,
reliable biometric recognition technology that makes it most popular than any
other biometric technique likes password, pin, fingerprint, etc. Many of the
governments across the world also interested in the face recognition system to
secure public places such as parks, airports, bus stations, and railway stations,
etc. Face recognition is one of the well-studied reallife problems. Excellent
progress has been done against face recognition technology
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4. Existing System:
Support Vector Machine
Discrete Wavelet Transform
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5. Drawbacks
 Existing face recognition solutions are no longer reliable when wearing a
mask.
Time consuming Process
Poor Detection
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6. Block Diagram
Input
Pre-
processing
Cropping
Face image
CNN
classification
obile set
modelsCaff
e m
Database
Without
mask
With Mask
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7. Preprocessing
Preprocessing is the data preprocessing and data augmentation module of
the Keras deep learning library. It provides utilities for working with image data, text
data, and sequence data
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8. CNN Structure
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9. Cnn classification
CNN description
Input Layer: This layer holds the raw input of image with width 32, height 32 and
depth
Convolution Layer: This layer computes the output volume by computing dot product
between all filters and image patch. Suppose we use total 12 filters for this layer we’ll
get output volume of dimension 32 x 32 x 12.
Activation Function Layer: This layer will apply element wise activation function to
the output of convolution layer. Some common activation functions are RELU: max(0,
x), Sigmoid: 1/(1+e^-x), Tanh, Leaky RELU, etc. The volume remains unchanged hence
output volume will have dimension 32 x 32 x 12.
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10. Continue…
Pool Layer:
This layer is periodically inserted in the covnets and its main functionis to reduce
the size of volume which makes the computation fast reduces memoryand also
prevents from overfitting. Two common types of pooling layers are maxpooling
and average pooling. If we use a max pool with 2 x 2 filters and stride 2,the
resultant volume will be of dimension 16x16x12.
 Fully-Connected Layer:
This layer is regular neural network layer which takesinput from the previous
layer and computes the class scores and outputs the 1-Darray of size equal to the
number of classes.
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11. Proposed System
Convolution Neural Network
Caffe Model
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12. Advantages
Highly Security
Its easily detection in mask
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13. Applications
Real time Applications
Airport
Train station
Bus stops….. etc
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14. Software Required
android studio
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