Quantum Based Face-Mask Detection

This Image shows how it detects the mask with the help of camera

Inspiration

Corona Virus pandemic is causing a global health crisis, According to the World Health Organization (WHO), the COVID-19 pandemic is producing a worldwide health catastrophe and the most effective protective technique is wearing a face mask in public places.

What it does

A mask face identification based on computer vision and deep learning. The suggested the model may be used in conjunction with security cameras to prevent COVID-19 transmission by detecting persons who aren't wearing face masks

How we built it

With OpenCV, tensor flow, and Keras, the model combines deep learning and traditional machine-learning approaches. For feature extraction, we coupled deep transfer learning with three traditional machine-learning methods. We conducted a comparison between them in order to identify the best appropriate algorithm that produced the best accuracy while consuming the least amount of train time.

Face\Mask Detection\in WEBCAM STREAM

The flow of the identity \person in\the webcam wear up the\the face\mask or not. The process\is in two ways:

Analyse the\mask on the\face
To Check the\mask on the\Webca In the webcam, identify the face and check the mask. A pre-prepared model provided by the OpenCV system was used to recognize the faces. The model was created from images found on the internet. This face finder has two models provided by OpenCV.

What's next for Quantum Based Face-Mask Detection

As technology advances and new trends emerge, we now have a unique face mask detector that may be useful in public healthcare. The backbone of the design is Mobile Net, which may be utilized for both high and low computation scenarios. We use transfer learning to adapt weights from a related problem, face detection, in order to extract more robust features, which are trained on a very large dataset.