A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic

The world is facing a huge health crisis due to the rapid transmission of coronavirus (COVID-19). Several guidelines were issued by the World Health Organization (WHO) for protection against the spread of coronavirus. According to WHO, the most effective preventive measure against COVID-19 is wearing a mask in public places and crowded areas. It is very difficult to monitor people manually in these areas. In this paper, a transfer learning model is proposed to automate the process of identifying the people who are not wearing mask. The proposed model is built by fine-tuning the pre-trained state-of-the-art deep learning model, InceptionV3. The proposed model is trained and tested on the Simulated Masked Face Dataset (SMFD). Image augmentation technique is adopted to address the limited availability of data for better training and testing of the model. The model outperformed the other recently proposed approaches by achieving an accuracy of 99.9% during training and 100% during testing.

Face Mask Detection using Transfer Learning of InceptionV3

The end of 2019 witnessed the outbreak of Coronavirus Disease 2019 (COVID-19), which has continued to be the cause of plight for millions of lives and businesses even in 2020. As the world recovers from the pandemic and plans to return to a state of normalcy, there is a wave of anxiety among all individuals, especially those who intend to resume in-person activity. Studies have proved that wearing a face mask significantly reduces the risk of viral transmission as well as provides a sense of protection. However, it is not feasible to manually track the implementation of this policy. Technology holds the key here. We introduce a Deep Learning based system that can detect instances where face masks are not used properly. Our system consists of a dual-stage Convolutional Neural Network (CNN) architecture capable of detecting masked and unmasked faces and can be integrated with pre-installed CCTV cameras. This will help track safety violations, promote the use of face masks, and ensure a safe working environment.

Multi-Stage CNN Architecture for Face Mask Detection

Coronavirus Disease 2019 (COVID-19) broke out at the end of 2019, and it's still wreaking havoc on millions of people's lives and businesses in 2020. There is an upsurge of uneasiness among people who plan to return to their daily activities in person, as the world recovers from the pandemic and plans to get back to a state of regularity. Wearing a face mask significantly reduces the risk of viral transmission and provides a sense of protection, according to several studies. However, manually tracking the implementation of this policy is not possible. The key here is technology. We present a Convolutional Neural Network (CNN) based architecture for detecting instances of improper use of face masks. Our system uses two-stage CNN architecture that can detect both masked and unmasked faces and is compatible with CCTV cameras. This will aid in the tracking of safety violations, the promotion of face mask use, and the creation of a safe working environment.

This paper introduces a system that detects the presence or absence of the mandatory medical mask in the operating room. The overall objective is to have as few false positive face detections as possible without losing mask detections in order to trigger alarms only for healthcare personnel who do not wear the surgical mask. The medical mask detection is performed with two face detectors; one of them for the face itself, and the other one for the medical mask. Both detectors run color processing in order to enhance the true positives to false positives ratio. The proposed system renders a recall above 95 % with a false positive rate below 5 % for the detection of faces and surgical masks. The system provides real-time image processing, reaching 10 fps on VGA resolution when processing the whole image. The Mixture of Gaussians technique for background subtraction increases the performance up to 20 fps on VGA images. VGA resolution allows for face or mask detection up to 5 m from the camera.

Adrian Nieto-Rodríguez

Papers

System for Medical Mask Detection in the Operating Room Through Facial Attributes