International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 05 | May 2022
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e-ISSN: 2395-0056 p-ISSN: 2395-0072
Face-Mask Alert System Using Transfer Learning Anchal Jain1, Vidushi Jain2, Shivam Sharma3, Rajesh Kumar Singh4 1,2,3 Student,
Dept. of Computer Science and Engineering, Meerut Institute of Engineering and Technology, India Dept. of Computer Science and Engineering, Meerut Institute of Engineering and Technology, India ----------------------------------------------------------------------***-------------------------------------------------------------------Abstract - In the year 2020, global spread of COVID-19 had put the lives of all the humans at risk. Medicines present were not 4 Professor,
helping enough. As a result there was a global shut down. The World Health Organization provided few guidelines to help control the spread of this disease. One of the guidelines included wearing masks at all public places. However, not everyone was following this. COVID-19 has made us realize the importance of face-masks. But even today, people are not taking this guideline seriously. Appointing people at all places to check if the guidelines are being followed is not a feasible solution to this issue. Hence, there is a need of some software that can do this work for us. Here we are using Transfer Learning to achieve this task. We have used MobileNet model as our base. The implementation is done in python, taking the help of tensorflow and keras. Each time the model detects a person not wearing a face-mask, it will make a beep sound to indicate the person to wear a mask. As soon as he/she puts on the mask, the beep will stop.
Keywords— Face-mask, MobileNet, Keras, COVID19, python 1.
INTRODUCTION
In the past few years, there had been life-threatening problems and many viruses all around the world like covid-19 and which has immense side effects and problems such as acute breathing syndrome(SARS CoV2) and it killed an immense amount of people to overcome this, one should maintain social distancing and the use of face masks is the key as masking the nose and mouth can limit the viruses through 95% so that masking is important for prevention and to check if someone is wearing a face mask or not is known as Face detection. In between these survival fights, we realized how much technology is important and it can be our lifesaver with all-day internet facilities and the use of face masks is very important in public like in schools, colleges, markets, shops, etc and a large amount of data is required for deep learning models to detect face mask, but to enforce the face mask on the public is difficult but with the help of Machine Learning, Artificial Intelligence, Open CV, Python, CNN to recognize if a person is protected or not. We use 2 phases here Training phase and the Application phase wherein first phase we train our model and in the second phase, we detect the images of a person if he is wearing a mask or not. This can help in shopping malls, Schools, and Colleges to check if the student is wearing a mask or not and the alarm got raised if the student is following Covid-19 rules.
2.
LITERATURE REVIEW
The Authors used the Principal Component Analysis method to identify faces with masks, which is a requirement in the field of security. This work is one of those works which was really important in this field of security. The accuracy in human face detection decreases by 70 % when a face mask is present. Now, The authors have developed a method to identify how a person is wearing the face mask. They now knew 3 categories of it, namely correct facemask-wearing, incorrect facemask-wearing, and no facemask-wearing. This method achieved over 98 % accuracy in detection. Here, we used Deep Learning as the method to check if a person is wearing a mask or not and in this project, we utilized the already existing solution as Transfer Learning. Transfer Learning is the method where we didn’t reinvent the wheel but used past methods to reinvent the solution out of it, We used multiple layers in it, a weight matrix to classify the Cat/Dog or Car/Truck which are similar but have different features. Also, if the problem is really similar freeze some of the layers and classify the rest of them. We have a dataset of 224*224 we can down-sample it so that the input size should be the same. We used CNN for image recognition and classification to detect objects, recognize faces, etc. In CNN we used Image Processing to enhance the image and extract some information from them. The basic understanding is Input->Image Output->Features associated with that image. There must be a Computer Vision so that we can see the image and view it the way humans are recognising it. Deep Learning takes input, assigns weights to objects, and that it is able to differentiate each other. We are using Softmax here to generate features out of it.
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