Face mask recognition system using CNN model

Abstract

COVID-19 epidemic has swiftly disrupted our day-to-day lives affecting the international trade and movements. Wearing a face mask to protect one's face has become the new normal. In the near future, many public service providers will expect the clients to wear masks appropriately to partake of their services. Therefore, face mask detection has become a critical duty to aid worldwide civilization. This paper provides a simple way to achieve this objective utilising some fundamental Machine Learning tools as TensorFlow, Keras, OpenCV and Scikit-Learn. The suggested technique successfully recognises the face in the image or video and then determines whether or not it has a mask on it. As a surveillance job performer, it can also recognise a face together with a mask in motion as well as in a video. The technique attains excellent accuracy. We investigate optimal parameter values for the Convolutional Neural Network model (CNN) in order to identify the existence of masks accurately without generating over-fitting.

Facial Mask Detection Using Depthwise Separable Convolutional Neural Network Model During COVID-19 Pandemic

“…Deep Learning models have been used in the majority of past research. After correctly recognizing the face in the picture or video, the CNN-based approach by Kaur et al ( 2 ) evaluates if the face has been disguised. It is also capable of identifying a moving face and a mask in a video as a surveillance job performance.…”

“…The ongoing pandemic and the rapidly emerging variants have made this respiratory illness, a daily headline. To prevent the spread of infection, it is recommended that people use face masks as part of their personal safety gear and as a public health measure ( 1 , 2 ). In light of this, the development of a system that can identify people wearing masks is critical in today's world.…”

“…Following the COVID-19 epidemic, other researchers developed their own techniques for monitoring face masks in common areas. Employing image processing algorithms, Surveillance systems are utilized for monitoring of public spaces in order to guarantee that no one's face is visible in crowded locations ( 2 ). Deep learning-based approaches for object identification and imagery analytics have been increasingly popular over the years.…”

“…The majority of the past research in object detection has been conducted using convolutional neural network models. Using deep learning architectures, convolutional neural networks (CNNs) have become more popular in recent years for a variety of tasks, such as picture identification ( 2 ), speech synthesis ( 7 ), object tracking ( 8 ), and image thresholding ( 9 ). When it comes to the abovementioned domains, CNN exhibits an excellent capacity to retrieve features from images.…”

“…Due to energy limitations, numerous deep neural networks are unsuited for mobile-based facial image classification since their evaluation phase is time consuming and expensive. We describe a MobileNet-based facial image classification model that uses a Depthwise separable convolution technique to handle this problem ( 2 ). DSC (Depthwise separable convolution) was first presented in ( 4 ) and is commonly used in image processing for classification tasks ( 10 ).…”