After the emergence of the Covid-19 pandemic in December of 2019, more than two years have gone. However, no one knows when it will be endemic. In different parts of the world, Covid-19 infection is going on and we are experiencing a sharp rise in it from time to time. So, it is essential for us (all people) to follow Covid appropriate behaviour until it becomes endemic.
At public places such as markets, big offices, and social event venues, ensuring everyone is wearing a face mask becomes a tough job for a concerned authority or security personnel. Wth technologies like AI (Artificial Intelligence) and Machine Learning, we have a face mask detector. Have a look at it.
What is face mask detection?
The objective of a face mask detection system is the determination of whether a person has put a mask on the face or not. And for this, the system relies on the clicked image or video. For the detection of face masks in a group or crowd, the technology encloses every face with a bounding box.
Modelling human faces is a tricky job due to the presence of variables that can change facial expression, lighting conditions, orientation and partial occlusions like mask, scarf, or sunglasses. The result of face mask detection produces the facial location parameters. And these parameters are in several forms such as eye centres, a rectangle covering the central facial part, or landmarks including nose, eyes, eyebrows, nostrils, and mouth corners.
What are the methods for face mask detection?
For face detection, there are two methods:
- Feature Base Approach – The recognition of objects happens due to their specific features. A human face has several features and their recognition is possible by detecting it among other objects. It identifies faces by extracting facial features such as nose, eyes, and mouth. Usually, there is qualification of some statistical classifiers for the separation of facial and non-facial regions. Further, human faces have specific textures that are used to distinguish a human face from other objects. Further, feature edges facilitate the detection of objects from the face.
- Image Base Approach – Usually, image-based methods rely on machine learning and statistical analysis techniques to find the relevant features of face and on-face images. The obtained characteristics are in the distribution model form or discriminant function form. In this method, technicians use several different algorithms like SVM, HMM, AdaBoost learning, or Neural Network.
How does a face mask detection system work?
The system has a process to detect people who are not having masks on their faces. Process for face mask detection is as follows:
- Check individual person or people in the crowd
- Use digital screens for reminding visitors to have masks on their faces
- Alert the concerned personnel if there is no mask detection
- Works with the use of existing IP or USB cameras with RTSP streams
- Anonymous & spoof proof
Common features of a face mask detection system
- Analysis for real-time face mask detection
- Crowds or individuals
- USB / IP / RTSP camera
- Linux & Windows
- Spoof proof detection
- Out-of-the-box technology
- Easily turn on and off
- Anonymous analysis
Application of face mask detectors
In the ongoing Covid-19 pandemic, every business sector and public place needs to keep a vigil on people and alert them while violetting Covid protocols. Here are some application areas of face mask detection systems:
- Public transport
- Corporate buildings
Accuracy of face mask detection system
Usually, its accuracy depends on vendors’ knowledge, experience, and skills on the technologies like Machine Learning, Artificial Intelligence (AI), and Internet of Things (IoTs). Its accuracy lies between 90 and 99%. Be careful while purchasing the system from a vendor.