This is a simple image classification project trained on the top of Keras/Tensorflow API with MobileNetV2 deep neural network architecture having weights considered as pre-trained 'imagenet' weights. The trained model (mask-detector-model.h5) takes the real-time video from webcam as an input and predicts if the face landmarks in Region of Interest (ROI) is 'Mask' or 'No Mask' with real-time on screen accuracy.

Face Mask Detector is used to automate the detection of face mask using images captured from a thermal camera. The problem is posed as a binary classification problem, wherein the input face image needs to be classified as with mask or without mask. Transfer learning is used for classification, wherein deep CNN model, MobileNetV2, is trained on a dataset of thermal face images with mask and without mask. The steps for building model are collecting the data, pre-processing, split the data, training the model, testing the model, and implement the model. The dataset is prepared using lepton FLIR camera interfaced to a Raspberry pi board. The built model can now detect people who are wearing a face mask and not wearing with an accuracy of 97 percent.

The Face mask detector didn't use any morphed masked images dataset. The model is accurate, and since we used the MobileNetV2 architecture, it’s also computationally efficient and thus making it easier to deploy the model to embedded systems (Raspberry Pi, Google Coral, etc.). This system can therefore be used in real-time applications which require face-mask detection for safety purposes due to the outbreak of Covid-19. This project can be integrated with embedded systems for application in airports, railway stations, offices, schools, and public places to ensure that public safety guidelines are followed.

A real time Face mask detector system based on Deep Learning using OpenCV ,Tensorflow/Keras and MobileNetv2

Our face mask detector doesn't use any morphed masked images dataset and the model is accurate. Owing to the use of MobileNetV2 architecture, it is computationally efficient, thus making it easier to deploy the model to embedded systems (Raspberry Pi, Google Coral, etc.).

This project aims to develop the face mask detector which is able to detect any kind of face mask. In order to detect the face mask, MobileNetV2 SSD(Single Shot multiBox Detector) is used which works well in detecting the object in real-time. Huge number of face images are used in this project for training the data.

Face Mask Detector using Python, Keras, OpenCV and MobileNetV2.

This is a face mask detector model built using mobilenetv2.

Face mask detection is planned model used to identify whether a person is wearing a mask or not in real-time,

Real time face mask detector using MobileNetV2 architecture and OpenCv library

Face mask detector using keras library, mobilenetV2 imagenet model and OpenCV

Real-time Face Mask Detection using MobileNetV2 and OpenCV with SSD face detector. Supports 3-class classification (with mask, without mask, incorrect mask).

Real-time face-mask detection using a MobileNetV2 classifier and OpenCV’s DNN face detector. Train on a folder-based dataset (with_mask / without_mask) and run live inference from your webcam.

During Covid pandemic, Government urged organization to wear mask in order to break the chain of transmission. In this regard, face mask detector was developed using MobilenetV2 Transfer Learning Technique

A simple Face Mask Detection app using Streamlit, TensorFlow/Keras, and OpenCV. It uses a pretrained SSD face detector and a MobileNetV2-based classifier to identify whether people are wearing masks in images or through a realtime webcam stream.

Face Mask Detector : Developed a deep learning model which identifies whether a person is wearing a mask or not. Used MobileNetV2 as base model and on top of it, customized model is added.

Face Mask Detector with Python. The face mask recognition in this project is developed by using a machine learning that will be using the image classification method: MobileNetV2.We will built a model. The built model can detect whether people are wearing a face mask or not. It will also ring an alarm whenever person is not wearing a mask.

This face mask detector is accurate, and since we used the MobileNetV2 architecture, it’s also computationally efficient, making it easier to deploy the model to embedded systems.

This project detects whether a person is wearing a mask in real-time using a webcam. It uses a MobileNetV2-based CNN trained on a dataset of masked and unmasked faces, combined with an OpenCV DNN face detector.

With the increasing number of COVID cases all over the world, a system to replace humans to check masks on the faces of people is greatly needed. This project makes the use of OpenCV , Caff e-based face detector , Keras , TensorFlow and MobileNetV2 for the detection of face mask on humans.

I will learn building a Face Mask Detector using Keras, Tensorflow, MobileNet and OpenCV. With further improvements these types of models could be integrated with CCTV cameras to detect and identify people without masks. The face mask detector didn't use any morphed masked images dataset. The model is accurate, and since the MobileNetV2 architecture is used, it’s also computationally efficient and thus making it easier to deploy the model to embedded systems (Raspberry Pi, Google Coral, etc.). This system can therefore be used in real-time applications which require face-mask detection for safety purposes due to the outbreak of Covid-19. This project can be integrated with embedded systems for application in airports, railway stations, offices, schools, and public places to ensure that public safety guidelines are followed.

In this Python programming I will building a Face Mask Detector using Keras, Tensorflow, MobileNet and OpenCV. We will also see how to apply this on a Live Video Camera. With further improvements these types of models could be integrated with CCTV cameras to detect and identify people without masks. The face mask detector didn't use any morphed masked images dataset. The model is accurate, and since the MobileNetV2 architecture is used, it’s also computationally efficient and thus making it easier to deploy the model to embedded systems (Raspberry Pi, Google Coral, etc.). This system can therefore be used in real-time applications which require face-mask detection for safety purposes due to the outbreak of Covid-19. This project can be integrated with embedded systems for application in airports, railway stations, offices, schools, and public places to ensure that public safety guidelines are followed.

In this Python programming video, we will learn building a Face Mask Detector using Keras, Tensorflow, MobileNet and OpenCV. We will also see how to apply this on a Live Video Camera. With further improvements these types of models could be integrated with CCTV cameras to detect and identify people without masks. The face mask detector didn't use any morphed masked images dataset. The model is accurate, and since the MobileNetV2 architecture is used, it’s also computationally efficient and thus making it easier to deploy the model to embedded systems (Raspberry Pi, Google Coral, etc.). This system can therefore be used in real-time applications which require face-mask detection for safety purposes due to the outbreak of Covid-19. This project can be integrated with embedded systems for application in airports, railway stations, offices, schools, and public places to ensure that public safety guidelines are followed.

In this project, a two-stage Face Mask Detector and Crowed counting is implemented. The First stage Crowed counting Using SDC-net model for doing crowd counting that is model learned in closed set and can be generalized to open set. It first generate the ground truth of input image then the density map, the do feature extraction by VGG16 model the divide the maps to count each part separately then evaluate the count for all parts then we append this counter to input image. The second stage uses a pretrained Retina Face model for face detection. Then training Face Mask Classifier models trying NasNet Mobile and MobileNetV2 on dataset and based on performance, the NasNet model was selected for classifying faces as masked or non-masked

The face mask detector didn't use any morphed masked images dataset. The model is accurate, and since the MobileNetV2 architecture is used, it’s also computationally efficient and thus making it easier to deploy the model to embedded systems (Raspberry Pi, Google Coral, etc.). This system can therefore be used in real-time applications which require face-mask detection for safety purposes due to the outbreak of Covid-19. This project can be integrated with embedded systems for application in airports, railway stations, offices, schools, and public places to ensure that public safety guidelines are followed.

Our face mask detector didn't use any morphed masked images dataset. The model is accurate, and since we used the MobileNetV2 architecture, it’s also computationally efficient and thus making it easier to deploy the model to embedded systems (Raspberry Pi, Google Coral, etc.). This system can therefore be used in real-time applications which require face-mask detection for safety purposes due to the outbreak of Covid-19. This project can be integrated with embedded systems for application in airports, railway stations, offices, schools, and public places to ensure that public safety guidelines are followed.