Real-Time Face Mask Detection and Analysis System

Singh, Juhi; Gupta, Lakshy; Kushwaha, Rahul; Varshney, Tanya; Chauhan, Akshat

doi:10.1007/978-981-16-6289-8_2

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Bae et al [31] also trained their encoder with the images of LFW. Singh et al [32] employed MobileNetV2 to determine the mask's presence. Sharma et al [33] combined infrared temperature sensors with a CNN and used the RMFD dataset to detect masked faces.…”

Section: Related Workmentioning

confidence: 99%

Fast and Accurate Detection of Masked Faces Using CNNs and LBPs

Alhammad,

Khafaga,

Hamed

et al. 2023

CSSE

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Face mask detection has several applications, including real-time surveillance, biometrics, etc. Identifying face masks is also helpful for crowd control and ensuring people wear them publicly. With monitoring personnel, it is impossible to ensure that people wear face masks; automated systems are a much superior option for face mask detection and monitoring. This paper introduces a simple and efficient approach for masked face detection. The architecture of the proposed approach is very straightforward; it combines deep learning and local binary patterns to extract features and classify them as masked or unmasked. The proposed system requires hardware with minimal power consumption compared to state-of-the-art deep learning algorithms. Our proposed system maintains two steps. At first, this work extracted the local features of an image by using a local binary pattern descriptor, and then we used deep learning to extract global features. The proposed approach has achieved excellent accuracy and high performance. The performance of the proposed method was tested on three benchmark datasets: the realworld masked faces dataset (RMFD), the simulated masked faces dataset (SMFD), and labeled faces in the wild (LFW). Performance metrics for the proposed technique were measured in terms of accuracy, precision, recall, and F1-score. Results indicated the efficiency of the proposed technique, providing accuracies of 99.86%, 99.98%, and 100% for RMFD, SMFD, and LFW, respectively. Moreover, the proposed method outperformed state-of-the-art deep learning methods in the recent bibliography for the same problem under study and on the same evaluation datasets.

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Section: Related Workmentioning

confidence: 99%

Fast and Accurate Detection of Masked Faces Using CNNs and LBPs

Alhammad,

Khafaga,

Hamed

et al. 2023

CSSE

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show abstract

“…This effectively keeps track of the patients' body temperatures on each ward. Juhi Singh et al [7] proposed an approach to detect face masks that have been created with computer vision utilizing OpenCV, Keras, Python, and TensorFlow. The main goals are to regularly verify whether people are wearing masks and to create weekly and monthly reports.…”

Section: IImentioning

confidence: 99%

IoT-Enabled System for Face-Mask Detection and Body Temperature Monitoring

Aakash¹

2022

IJRASET

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The world is entering the third year of the most acute health crisis and remains in its grip. Instead of meeting in the aftermath of the pandemic, a fresh wave of cases and deaths are met. The COVID-19 impact is everywhere, resulting in the closure of Schools and other educational institutions. The COVID-19 epidemic has an impact on both off-screen and on-screen entertainment, as well as work activities, interpersonal interactions, all sports activities, and other recreational pursuits. Because there is a greater danger of infection, people with high body temperatures should not be allowed in public areas. Also required at all city, business, school, and college entrances are mask inspections and temperature checks. The suggested work is being developed as a prototype to identify masks and keep an eye on people's body temperatures, particularly kids riding in buses

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Review Paper on Face Mask Detection with Body Temperature Monitoring Using IoT

Tewar

Patel

Deshmukh

et al. 2023

IJSRST

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The huge family of viruses known as coronaviruses, which are common, contagious, and hazardous to all humans, have become more prevalent in recent years. It spreads from one person to another by exhaling the contagious breath, which then deposits virus droplets on various surfaces. These droplets are then breathed by additional persons, who eventually become infected. As a result, it is essential that we protect both ourselves and our loved ones from this situation. We may practice safety measures like keeping a safe distance from others, washing our hands every two hours, applying hand sanitizer and most importantly wearing a mask. Therefore, as a precaution, the World Health Organization (WHO) suggested wearing masks in crowded locations. In some places, infections have spread quickly due to improper facial mask usage. We required a dependable solution for mask monitoring to overcome this problem. Face mask detection software based on AI and image processing techniques can help government organizations that are working to make wearing a face mask required. For face detection, helmet detection, and mask detection, the approaches mentioned in the article utilize Machine learning, Deep learning, and many other approaches. It will be simple to distinguish between persons having masks and those who are not having masks using all of these ways. The effectiveness of mask detectors must be improved immediately.

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Real-Time Face Mask Detection and Analysis System

Cited by 3 publications

References 6 publications

Fast and Accurate Detection of Masked Faces Using CNNs and LBPs

Fast and Accurate Detection of Masked Faces Using CNNs and LBPs

IoT-Enabled System for Face-Mask Detection and Body Temperature Monitoring

Review Paper on Face Mask Detection with Body Temperature Monitoring Using IoT

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