khadar25 / face-mask-detection

using mobilenet_v2

Face-Mask-Detection

Face Mask Detection system built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect face masks in static images as well as in real-time video streams.

Tech/framework used

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

Features

This face mask detector didn't uses 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.

Dataset

This dataset consists of 3835 images belonging to two classes:

• with_mask: 1916 images
• without_mask: 1919 images

The images used were real images of faces wearing masks. The images were collected from the following sources:

• Kaggle datasets

All the dependencies and required libraries are included in the file requirements.txt

Working

• Open terminal. Go into the cloned project directory folder and type the following command:
  $ python3 train_mask_detector.py --dataset dataset
• Now detect the face masks in images
  $ python3 detect_mask_image.py --image images/pic1.jpeg
• Detection in real-time video streams
  $ python3 detect_mask_video.py

Results

• Our model gave 93% accuracy for Face Mask Detection after training via tensorflow-gpu==2.0.0

We got the following accuracy/loss training curve plot