The document proposes a machine learning model to detect whether individuals are wearing face masks in public places. It divides the strategy into two parts: face detection followed by mask detection on detected faces. The proposed model includes pre-processing, face detection, feature extraction, classification, and training stages. The objectives are to develop an automated system using artificial networks to identify if people in public are wearing masks, in order to enforce proper usage and combat the COVID-19 pandemic.

1. FACE MASK DETECTION USING
MACHINE LEARNING
By:
G JAYASURYA
41130194
ECE

2. INTRNSHIP CERTIFICATE

3. INTRODUCTION
• Mask detection in public place means to identify whether a person is wearing a mask or not. The first
step to recognize the presence of a mask on the face is to detect the face, which makes the strategy
divided into two parts: to detect faces and to detect masks on those faces.
• Face detection is one of the applications of object detection and can be used in many areas like security,
biometrics, law enforcement, in traffic while driving, Airport and more.
• There are many detector systems developed around the world and being implemented. However, all this
science needs optimization; a better, more precise detector, because the world cannot afford any more
increase in corona cases.

4. PROPOSED WORK
1. The input image
2. The Pre-processing Stage
3. The face Detection Stage
4. The Feature-Extraction Stage
5. The Classification Stage
6. Training Stage
7. Prediction Stage

5. OBJECTIVES
 The world is struggling with Covid-19 pandemic and are so many essential equipment needed to
combat against Corona virus. One of such most essential is Face Mask and mask was not mandatory for
everyone but as the day surpasses scientist and Doctors have recommended everyone to wear the mask.
Therefore, to detect whether a person is wearing Face Mask or not, there are detection technique. Mask
Detection in Public Place Platform utilizes Artificial Network to identify if a person does/doesn’t wear
a mask. The application can be associated with any current or new IP cameras to identify individuals
with/without a mask
 It automates the process to keep a vigilance on a crowd to enforce proper face mask usage.

6. Dataset
 We download dataset from kaggle. Dataset contains two
folders. One folder contains images of people wearing
mask and other folder contains images of people not
wearing mask.

7. Dataset with face mask

8. Dataset without face mask

9. Training the model