In the year 2019, the coronavirus or Covid-19 pandemic situation began to ruin our daily lives. As a result, WHO, and other organizations tried to find new ways to keep us protected. The face mask was declared as a major solution to stop the virus spread through the air. But, companies started to face another major challenge; face mask detection.
Even after Covid-19 ends and people started to continue their new-normal lives, using face masks has become a common safety practice. There are plenty of underdeveloped as well as developed nations that have made it compulsory for their people to use a face mask whenever they are leaving their houses and visiting any public places.
However, using a face mask may have safety benefits but it created a huge challenge for business leaders especially in the healthcare, retail, and entertainment sector to identify their employees who have their faces covered with a mask. This led them to come up with new innovative digital solutions to upgrade the current face detection algorithms.
In this article, we will help you get through the current face mask detection system that companies are leveraging and how the entire system works in today's time. So, let's dive in,
Even after the huge evolution of the Internet of things or IoT technology, companies were still struggling to identify their employees who have put their masks on amidst the pandemic and even after the pandemic, a new standard model was introduced to improve the facial recognition system. The approach was developed with the use of data augmentation.
The current model focuses primarily on generating an augmented dataset from a standard dataset using data augmentation conducted by using a image filtering technique like Gaussian blur or grayscale. The augmented dataset is further used to train the object for mask detection. Since the training method includes capturing the maximum amount of data variation, it helps to create a better generalization in the model. As a result, the average precision in this system observed was 99.8% during the training.
Even after the exceptional efficiency of 3D and 2D recognition, the earlier face recognition system based on DNN used to face several challenges such as difficulty in collecting training images. It's because DNN used to require a huge amount of data to understand various face views for each subject. However, this was quite challenging for most businesses to obtain such a dataset. Not only was it challenging but also it was pretty time-consuming. So, to train face samples in multiple conditions, poses, and facial expressions, an efficient and effective solution, a data augmentation technique is used.
The purpose of this data augmentation is basically to expand the training database's diversity and size and help the model to expose to multiple aspects of data to guarantee higher accuracy and stable performance. Image augmentation is generally categorized as a generative or traditional augmentation that used to involve geometric transformation, color space augmentation, kernel filters, random cropping, noise injection, etc. However, the current data augmentation method is based entirely on face representation with an adaptive fusion of softmax loss and center loss.
The system works on these three parameters:
Find facial landmarks
Analyzing the present scenario, there are plenty of use cases of face mask detection systems including offices like manufacturers, SMEs, retail industries, healthcare organizations, railway and airports, entertainment industries, sports venues, or any other densely populated areas. Generally, even after the Covid-19 pandemic has ended, private and government organizations still want to ensure that employees and everyone visiting the offices use a face mask throughout the day.
Therefore, having a face mask detection system is crucial that can quickly identify a person who has his face covered with a mask. In fact, with modern technology, you can add your employee contact details like email addresses and phone numbers to help the system send an alert to those who have not worn the mask.
Detecting masked faces is certainly a challenging task due to its complexity. However, with the help of advanced technology like data augmentation, artificial intelligence, and ML or machine learning, companies have developed the ideal system to detect masked faces. Basically, when it comes to detecting masked faces, it requires a system that can easily identify the datasets of the masked faces like facial features.
There are a lot of benefits of using a face mask detection system and it includes:
These are some of the benefits listed above. Apart from that, there are endless other perks of using a face mask detection system to identify employee's faces covered with masks.
Face recognition is a pretty simple method of identifying faces including all the features through the use of technology. However, the facial mask recognition system is a bit challenging. But, as the new demand is emerging in the market, companies are coming up with unique solutions for the mask detection system.
But, in this case, types of masks, location of masks, location of eyes, face orientation, location of faces, and occlusion degree all play an important role to achieve the highest level of accuracy. Corporate giants are turning to ML and AI to launch their innovative face mask detection services to enable others to recognize faces covered with a mask. Contact us to know more.