COVID-19 Face Mask Live Detection Using OpenCV

Dumala, Anveshini; Papasani, Anusha; Vikkurty, Sireesha

doi:10.1007/978-981-16-1502-3_35

Cited by 9 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…OpenCV has around 47,000 users and 18 million downloads. Companies, research groups, and governments use the library [5].…”

Section: Tensorflow Algorithmsmentioning

confidence: 99%

Segmentation and classification of specific pattern of Brain tumor using CNN

Alshanketi¹

2023

IJETMS

View full text Add to dashboard Cite

During the Covid-19 pandemic, it was compulsory to wear a mask. Even in the current situation, when the pandemic is not over. A mask is essential and can be mandated from time to time for specific purposes and conditions. Manually monitoring the masks for large numbers of visitors or attendees for an event or at any public place is challenging and requires much manual work. It is time-consuming, and we cannot completely monitor it. Machine learning based approaches and automated monitoring using security cameras can be a possible solution to minimize the need for large manpower. Many models have been proposed for mask detection using machine learning but still there is a chance for improving the accuracy. Further the training time is high in these cases were normally the train and test ratio for dataset is 80:20 that takes a good chunk of time in training. This study proposes a machine learning-based face detection mechanism using MobileNet_v2 transfer learning techniques to provide very high accuracy in detecting the people not wearing masks or not wearing them properly. Further this study proposes to minimize the training time for speedy and real-time analysis by selecting an optimal combination of train and test dataset ratio. Finally, the model has been implemented using the webcam tool to recognize mask wearers in real time with high accuracy.

show abstract

“…OpenCV has around 47,000 users and 18 million downloads. Companies, research groups, and governments use the library [5].…”

Section: Tensorflow Algorithmsmentioning

confidence: 99%

Segmentation and classification of specific pattern of Brain tumor using CNN

Alshanketi¹

2023

IJETMS

View full text Add to dashboard Cite

show abstract

“…Ukuran droplet umumnya sebesar 5 um sampai 10 um. [1] Salah satu bentuk pencegahan penyebaran dan perlindungan terhadap penyebaran adalah dengan pemakaian masker. pada sebuah studiisehingga mengurangiipenyebaran jika upaya pencegahan tersebut diilakukan dengan benar.…”

Section: Pendahuluanunclassified

Face Mask Recognition Menggunakan Model CNN (Convolutional Neural Network) Berbasis Python dan OpenCV

Putra,

Triayudi,

Ningsih

2023

JoSYC

View full text Add to dashboard Cite

During the COVID-19 pandemic, masks are one of the main measuring tools in carrying out health protocols, masks are also a top priority when carrying out activities outside the home or office. Because masks are quite effective in filtering out disease particles that allow users not to get infected. Therefore, many places have made masks an important requirement in maintaining health protocols during the COVID-19 pandemic. Previously there was a system that had been created to assist the government in implementing the mandatory wearing of masks, but there were still deficiencies. Therefore the authors created a system to detect mask wearing by updating previous researchers using the convolutional neural network (CNN) algorithm. For making this system the author uses the PYTHON and OPENCV programming languages. which will produce four parts in this detection, namely Mask, No Mask, Covered Mouth Chin and Covered Nose Mouth.

show abstract