Mask or Non-Mask? Robust Face Mask Detector via Triplet-Consistency Representation Learning

Abstract

In the absence of vaccines or medicines to stop COVID-19, one of the effective methods to slow the spread of the coronavirus and reduce the overloading of healthcare is to wear a face mask. Nevertheless, to mandate the use of face masks or coverings in public areas, additional human resources are required, which is tedious and attention-intensive. To automate the monitoring process, one of the promising solutions is to leverage existing object detection models to detect the faces with or without masks. As such, security officers do not have to stare at the monitoring devices or crowds, and only have to deal with the alerts triggered by the detection of faces without masks. Existing object detection models usually focus on designing the CNN-based network architectures for extracting discriminative features. However, the size of training datasets of face mask detection is small, while the difference between faces with and without masks is subtle. Therefore, in this article, we propose a face mask detection framework that uses the context attention module to enable the effective attention of the feed-forward convolution neural network by adapting their attention maps’ feature refinement. Moreover, we further propose an anchor-free detector with Triplet-Consistency Representation Learning by integrating the consistency loss and the triplet loss to deal with the small-scale training data and the similarity between masks and occlusions. Extensive experimental results show that our method outperforms the other state-of-the-art methods. The source code is released as a public download to improve public health at https://github.com/wei-1006/MaskFaceDetection .

An Accurate Real-Time Method for Face Mask Detection using CNN and SVM

“…Notably, the proposed model achieved a higher recall of 93%. The recall is improved by 1% [21], 2% [13], 3% [19], 5% [20], and 6% [18].…”

“…The suggested method was tested against various pre-existing models on the same datasets, and the findings are presented in this study. Five contemporary approaches [18][19] [20][13] [21] were chosen for this purpose. Table 1 compares several models with the suggested one using the accuracy metric.…”

“…In particular, the suggested model attained a greater accuracy of 94% compared to previous techniques. The accuracy is improved by 3% [21], 5% [19], 6% [13], 8% [18], and 10% [20]. 2 compares several models with the suggested one using the precision metric.…”

Mask Wearing Status Estimation with Smartwatches

“…Some works are proposed to detect whether people are wearing masks with the algorithms on images or videos [9]- [11], but these works can only be applied at places where cameras are deployed, thus cannot give users a timely reminder to help them wear masks to block the spread of the virus. In [12], self-compliance with personal management regulations and conscious wearing of masks are more effective in preventing the spread of infectious diseases than from outside supervision.…”

Face Mask-Wearing Detection Model Based on Loss Function and Attention Mechanism

“…The spread of COVID-19 endangers public safety, and its known transmission routes mainly include droplet transmission and close contact. Therefore, wearing masks has become the main means of preventing the spread of the virus [ 1 , 2 ]. The traditional methods of inspecting mask-wearing are mostly manual inspections, which not only consume manpower and material resources and are inefficient but also cause certain difficulties in personnel management, which is more likely to cause crowds to gather and increase the risk of virus spread.…”