Masked Face Detection and Calibration with Deep Learning Models

Li, Xingze

doi:10.1088/1742-6596/2196/1/012011

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…On the basis of this prediction model, a mask recognition system is designed to realize real-time mask detection in open environments and large-traffic scenarios. Different from other projects, data smoothing and Mosaic data enhancement are added in the training process of this project, which improves the recognition accuracy and training time to a certain extent [4] .…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of different scene for face mask detection based on YOLOv4

Cheng¹,

Zhang²,

Zhu³

2023

Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)

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The 2019-nCoV can be transmitted through respiratory droplets and other methods, which greatly endangers public health security. Wearing masks correctly has been proven to be one of the effective means to prevent virus infection, but limited by the complexity of practical application scenarios, the wearing of masks still relies heavily on manual supervision. Therefore, a fast and accurate face mask wearing detection method is urgently needed. In this paper, a mask detection algorithm based on improved YOLO-v4 is proposed as a solution to the problems of low accuracy, poor real-time performance, and poor robustness caused by complicated environments. In addition, a number of different training approaches, such as mosaic data augment, CIOU, label smoothing, cosine annealing, etc., are introduced. These techniques help to increase the training speed of the model as well as the accuracy of its detection. With a fast-training model, the model will be able to detect and compare the results of samples from different scenarios. The experiment will compare front and side faces, different colored masks, scenes of varying complexity and other perspectives in a systematic way. The experiment's result was able to reach 99.38 % accuracy after the model was trained using data from a variety of face masks being worn. Experiment results, both quantitative and qualitative, indicate that the method can be adapted to most scenarios and offers effective ideas for improvement.

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Section: Introductionmentioning

confidence: 99%

Comparative analysis of different scene for face mask detection based on YOLOv4

Cheng¹,

Zhang²,

Zhu³

2023

Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)

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“…Furthermore, several public service providers restrict customers to utilize their services while wearing facemasks that meet specific standards. However, due to the face mask covering the majority of the crucial facial characteristics, such as the nose and mouth, conventional face recognition systems used for security have proven ineffective in the current circumstance making it exceedingly challenging to identify the person [2].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach for Masked Face Identification

Shatnawi¹,

Almenhali²,

Alhammadi³

et al. 2022

IJACSA

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Covid-19 is a global health emergency and a major concern in the industrial and residential sectors. It has the ability to spread leading to health problems or death. Wearing a mask in public locations and busy areas is the most effective COVID-19 prevention measure. Face recognition provides an accurate method that overcomes uncertainties such as false prediction, high cost, and time consumption, as it is understood that the primary identification for every human being is his face. As a result, masked face identification is required to solve the issue of recognizing individuals with masks in several applications such as door access systems and smart attendance systems. This paper offers an important and intelligent method to solve this issue. We propose deep transfer learning approach for masked face human identification. We created a dataset of masked-face images and examined six convolutional neural network (CNN) models on this dataset. All models show great performance in terms of very high face recognition accuracy and short training time.

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