Real-time face mask detection for COVID-19 prevention

Sujon, Mohammad Rezaul Karim; Hossain, Md. Rasel; Amin, Md. Jaki Al; Bepery, Chinmay; Rahman, Md. Mahbubur

doi:10.1109/ccwc54503.2022.9720764

Cited by 5 publications

(2 citation statements)

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“…Another researcher who used Viola Jones for face detection was (Hassan & Dawood, 2022) (Florestiyanto et al, 2020;Pratama, 2022;Suharso, 2017) for real-time with 67.6% accuracy. Research by (Fendi et al, 2020) has 90.9% accuracy (Karim Sujon et al, 2022). Therefore, it is possible to develop a threshold method that can detect faces to increase the accuracy of face detection…”

Section: Introductionmentioning

confidence: 99%

Masked Face Detection Automation System Using Mask Threshold and Viola Jones Method

Nilatika¹,

Anwar²,

Yuniar³

2022

J. Ris. Inform.

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Reducing or even breaking the chain of Covid-19 virus infections during a pandemic is important. The techniques that are encouraged are mandatory hand washing, social distancing, and mandatory wearing of masks. Wearing masks is urgent, therefore requiring people to wear masks is the right policy. This study aims to detect people who use masks or do not use masks by applying the Viola Jones method. In this study, modification of the tresholder algorithm was carried out by applying a mask thresholder for optimization of facial segmentation. Meanwhile viola jones was built by combining several concepts of Haar Feature, Integral Image, AdaBoost, classivier Cascade into a main method for detecting objects. The performance of the proposed method for face detection has an accuracy of 95%, a precision of 94.73%, and a recall of 100%. 5. The masked face detection test has an accuracy of 94%, precision 100%, and recall 90.90%

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Section: Introductionmentioning

confidence: 99%

Masked Face Detection Automation System Using Mask Threshold and Viola Jones Method

Nilatika¹,

Anwar²,

Yuniar³

2022

J. Ris. Inform.

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“…masked, unmasked, and improperly masked faces. CNNs models trained by transer learning approches for the face mask detection system involve the usage of the effectively pretrained models trained by feeding a large image dataset [17]- [21]. Although initial weights of these pretrained models, e.g.…”

mentioning

confidence: 99%

Face mask classification using convolutional neural networks with facial image regions and super resolution

Wattanakitrungroj,

Wettayaprasit,

Rujirapong

et al. 2024

IJ-AI

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<p>Face mask classification is relevant to public health and safety, so an approach for face mask classification using Multi-Task Cascaded Convolutional Networks (MTCNN) for face detection on image data, ResNet152 architecture for feature extraction, and super-resolution method, BSRGAN, for enhanced image quality was proposed. The classification model was trained by a fully connected layer of neural networks. The goal is to classify each facial image into three classes: the image with a mask, without a mask, or with an incorrectly worn mask. The performance of each classification model on two real-world datasets was evaluated by Accuracy, Precision, Recall, and F1 score for different sets of input patterns which were features extracted from the facial image regions including their combinations. Using multiple image regions, i.e. face, nose, and mouth, as resources for preparing input features showed the improved classification performance compared to using single image regions. In addition, the super-resolution technique applied to medium or large-sized images can improve the performance of the face mask classification model. Our findings may further guide the development for greater effective models and techniques on face mask classification contributing to practical scenarios.</p>

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