Face Mask Detection Based Entry System

Changes in the lifestyle of everyone around the world. In those changes wearing a mask has been very vital to every individual. Detection of people who are not wearing masks is a challenge due to Outbreak of the Coronavirus pandemic has created various the large number of populations. This project c

Project Title

Face Mask Detection Based Entry System

Project Area of Specialization

Internet of Things

Project Summary

Changes in the lifestyle of everyone around the world. In those changes wearing a mask has been very vital to every individual. Detection of people who are not wearing masks is a challenge due to Outbreak of the Coronavirus pandemic has created various the large number of populations. This project can be used in schools, hospitals, banks, airports, and etc. as a digitalized scanning tool. The technique of detecting people’s faces and segregating them into two classes namely the people with masks and people without masks is done with the help of image processing and deep learning. With the help of this project, a person who is intended to monitor the people can be seated in a remote area and still can monitor efficiently and give instructions accordingly. Various libraries of python such as OpenCV, Tensorflow and Keras. In Deep Learning Convolution Neural Networks is a class Deep Neural Networks which is used to train the models used for this project.Changes in the lifestyle of everyone around the world. In those changes wearing a mask has been very vital to every individual. Detection of people who are not wearing masks is a challenge due to Outbreak of the Coronavirus pandemic has created various the large number of populations. This project can be used in schools, hospitals, banks, airports, and etc. as a digitalized scanning tool. The technique of detecting people’s faces and segregating them into two classes namely the people with masks and people without masks is done with the help of image processing and deep learning. With the help of this project, a person who is intended to monitor the people can be seated in a remote area and still can monitor efficiently and give instructions accordingly. Various libraries of python such as OpenCV, Tensorflow and Keras. In Deep Learning Convolution Neural Networks is a class Deep Neural Networks which is used to train the models used for this project.Changes in the lifestyle of everyone around the world. In those changes wearing a mask has been very vital to every individual. Detection of people who are not wearing masks is a challenge due to Outbreak of the Coronavirus pandemic has created various the large number of populations. This project can be used in schools, hospitals, banks, airports, and etc. as a digitalized scanning tool. The technique of detecting people’s faces and segregating them into two classes namely the people with masks and people without masks is done with the help of image processing and deep learning. With the help of this project, a person who is intended to monitor the people can be seated in a remote area and still can monitor efficiently and give instructions accordingly. Various libraries of python such as OpenCV, Tensorflow and Keras. In Deep Learning Convolution Neural Networks is a class Deep Neural Networks which is used to train the models used for this project.

Project Objectives

The face mask detection platform will quickly identify the person with a mask, using cameras and analytics. Depending upon the requirements, the system is also adaptable to the latest technology and tools i.e.; you can add contact numbers or email addresses in the system to send an alert to the one who has not worn the mask. You can also send an alert to the person whose face is not recognizable in the system.

Project Implementation Method

Step:1 Data Visualization:

                                          In the first step,  visualize the total number of images in the dataset in these two categories.If there are images of people with mask ,they will  in the “Yes” category and those without mask will be in the “No” category.

Step:2 Data Augmentation:

                                           In the next step, we expand the data set to include a larger number of images for training. In this step of data expansion, we rotate and flip each image in the data set.

Step:3 Splitting Data:

                                           In this step, we divide the data into a training set, and the training set will contain the images on which the CNN model will be trained and test set and the images on which the model will be tested. In this case, we use split_size =0.8, which means that 80% of the total images will enter the training set, and the remaining 20% of the images will enter the test set.

After segmentation, we see that the required image percentage has been allocated to the training set and test set.

Step:4 Building the model:

                                           In the next step, we will use Conv2D, MaxPooling2D, Flatten, Dropout, and dense to build a sequential CNN model. In the last dense layer, we use the “ soft ax ” function to output vector that gives the probability of each of the two categories.

Step:5 Pretraining the CNN Model:

                                                        After setting up the model we will create”train generator” and “validation_generator” to make it fit our model in the next step.

Step:6 Training the CNN Model:

                                                    This is the main step in which we put Images into the training set and test set to use the sequence model built by the keras library.

Step:7 Labeling the information:

                                                    After building the model, we label the results with two probabilities.[“0” is “without mask”, “1” is “with_mask”]. I also set the color of the bounding rectangle using RGB values. [“RED” stands for “without mask” “GREEN” stands for “with mask”’].

Step:8 Importing the Face Detection Program:

                                                                         From now on, we plan to use it to detect whether we are wearing a mask through the pc’s webcam. For this, first of all, we need to implement face detection.

Step:9 Detecting Face With or Without Mask:

                                                                         In the last step, we use the OvenCV library to run an infinite loop to use our webcam, where the cascade classifier is used to detect faces. The code webcam =cv2.videoCapture (0) indicates the usage of the webcam. The model will predict the likelihood of each of the two categories [without mask, with mask]). Based on a higher probability, tags will be selected and displayed around our face.

Benefits of the Project

  o Easy to use and configure

 o Very fast processing speed

 o Automatic detection of people who are not wearing a face mask

o Saving time and energy

o Decreasing manpower cost

o High accuracy and recognition rates o Increased security and safety

Technical Details of Final Deliverable

Framework Used:

• • • OpenCV
    • Caffe-based face detector
    • Keras
    • TensorFlow
    • MobileNetV2

Final Deliverable of the Project

HW/SW integrated system

Core Industry

IT

Other Industries

Core Technology

Internet of Things (IoT)

Other Technologies

Artificial Intelligence(AI)

Sustainable Development Goals

Good Health and Well-Being for People, Sustainable Cities and Communities

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com