Companies need to continually improve their existing infrastructure and processes to be
more efficient, safe, and usable for employees, customers, and the community. We know face masks can essentially reduce hazardous viruses’ transportation, but sometimes people neglect or forget to wear them properly.
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The main goal or objective of face mask detection is to identify the person in the image/video stream wearing a face mask or not; if wear, then determine wearing properly or not with the help of Artificial Intelligence techniques like computer vision and deep learning algorithms.
As the country starts working through various reopening stages, face masks have become an essential part of our daily lives and stay here. Using face masks (and wearing them correctly) will be needed to socialize or handle business. Most importantly, it is the most reliable way to overcome the transmission of the virus.
According to the World Health Organization( as of July 12, 2020 report), the current explosion of COVID-19 has affected over 13,039,853 people and more than 571,659 deaths in more than 200 countries worldwide. It has increased quickly across the world, bringing massive health, economic, environmental, and social challenges to the entire human population. At that time, WHO suggests people should wear face masks to avoid the risk of virus transmission, and a social distance of at least 2m be kept between individuals to prevent the person-to-person spread of disease.
At the starting stage of the pandemic situation, many experts advised against the usage of facemasks by society because of their potential risks, such as self-contamination. Experts then changed their thinking about masks’ potential advantages to include protecting others against infection with SARS-CoV-2 (source control), similar to how medical masks in the operating room protect patients.
With COVID-19, however, face masks might help protect both healthcare workers and the public. Systematic reviews of facemask use suggest relative risk reductions for infection ranging from 6–80%, including for betacoronavirus infection (e.g., COVID-19, SARS, MERS). The current best evidence includes the possibility of important relative and absolute benefits of wearing a facemask.
Covering our faces with a mask has become a new normal amidst the pandemic, as face masks effectively stop the virus outbreak. Many developed and underdeveloped nations worldwide have made it mandatory for people to wear masks if leaving home or visiting public places. Many public service providers require their customers to use the service only if customers wear masks and follow safe social distancing. Therefore, face mask detection and safe social distance monitoring have become crucial computer vision tasks to help the global society.
How can a company/industry recognize its employees with masks? The same inquiries may arise for other domains, like retail, healthcare, transportation, entertainment, etc. And the solution to this can be the Face Mask Detection system.
Face recognition is an easy way to identify faces, including facial characteristics through technology, primarily hardware, like video cameras. Government enterprises are increasing the demand for surveillance systems to improve security and will increase AI-based facial recognition technology appropriation.
Among the global change, new demand has appeared in the market, and that is face mask detection. Analyzing the current scenario, government and private businesses critically needed to check or confirm that everyone working or touring a public or private place is wearing masks during the day to overcome the spread of the virus. The AI-based face mask detection system can quickly identify the person with a mask using cameras and analytics.
Face Mask Detection is an AI and Computer Vision inspired image analytics solution that provides to the Covid-19 related violations. The face mask detection model uses visible stream from the camera combined with AI techniques to detect and alert people not wearing face masks.
We collect several images of people who are not wearing masks, wearing masks, and not properly wearing a mask of different kinds of masks. We extract all these kinds of images by extracting them from different sources or from machines (cameras) of different resolutions.
We annotate the extracted raw images with three kinds of labels such as masks, no mask, and not wearing properly for this problem statement. This information will help the model to recognize the face with a mask or not.
How does the face mask detection model learn and detect our targeted information from every image or video clip? Here the annotation process will perform a crucial role. Just think about how vital the annotation process is to the model or system. Face mask detection models should not identify target output without high quality annotated dataset.
The annotator team annotated the extracted images by drawing bounding boxes around them along with the detected class name, such as wearing a mask, not wearing a mask, and not correctly wearing a mask.
For this, we should provide better-annotated images to the model while building. Without an annotation, the model can’t know where it will take patterns and then learn. So automatically, AI-based models shouldn’t discover any patterns to recognize our target outputs.
The images extracted from the different sources required preprocessing before proceeding to the next step. Data preprocessing is the set of procedures used to apply on the raw extracted input images to transform them into clean versions, which could be fed to Artificial Intelligence models or neural network machine learning models.
In the image data preprocessing step, the image is converted into a grayscale image because the RGB (Red Green Blue) color image carries so much repetitive or irrelevant information that is not necessary for face mask detection. Then, we reshaped the images to keep the uniformity of the input images to the architecture. Then, the images are normalized, and after normalization, the value of a pixel stays in the range from 0 to 1. Normalization improved the learning algorithm to learn faster and captured the necessary features from the images.
Identifying multiple objects like masked and unmasked faces in images can be solved by a traditional object detection model. The process of object detection primarily includes localizing the objects in images and classifying them.
This face mask detection model follows two main stages. The first step of this model architecture involves a Face Detector, which identifies multiple faces in images of different sizes and detects faces even in overlapping situations. The detected faces are extracted from this stage are then batched together and transferred to the second stage of face mask detection model architecture, a computer-vision-based deep learning neural network Face Mask Classifier. The second stage results are decoded, and the final result is the image with all the detected faces in the image and classified as a target label, either masked or unmasked faces.
A face detector model acts as the first step of face mask detection model architecture. The preprocessed images are passed as the input to this stage—the face detector extracts and displays all the human faces detected in the input image with their bounding box coordinates. The process of accurately detecting faces is very important for this model architecture. Building a highly accurate or performance value face detector requires a lot of labeled datasets, time, and compute resources. The face detection process is challenging for the model used in this first stage of the model architecture. It demands the detection of human faces that could also be covered with masks.
The second stage of this face mask detection model is a face mask classifier. This stage takes input as the processed or detected face-related features from the previous stage’s output. This classifier model classifies faces either wearing a mask or no mask.
Face mask detection systems help to ensure people’s safety in public places by automatically monitoring whether they use a face mask or not. We can integrate this system or model with an image or video-catching device like a CCTV camera. This way, we can track safety violations, increase the use of face masks, and ensure a safe working environment. Suppose the face mask detector application identifies a user that he/she was not wearing a mask. In that case, AI-based face mask detection model alerts are sent to the particular person with the person’s picture or image. It supports the application to manage automatically and enforces the wearing of the mask.