Face Mask Recognition Based on MTCNN and MobileNet

Cao, Jianzhao; Pang, Renning; Ma, Ruwei; Yue, Qi

doi:10.1007/978-981-33-4575-1_41

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…To demonstrate the effectiveness of our proposed method, we carried out several experiments based on the above dataset, obtained the results, and analyzed them thoroughly. The approach described in reference [6], referred to as M2, employs MTCNN and MobileNet models for face detection and mask recognition, respectively, with a similar implementation to our method, referred to as YM for short. So, we made a comparison between these two.…”

Section: Experiments Results and Analysismentioning

confidence: 99%

“…First, locate the positions of faces in an image; then, identify whether the faces in the image are wearing a mask. The studies by Tang et al [6] and Cao et al [7] proposed a face mask recognition method based on multi-task convolutional neural networks (MTCNN) and MobileNet, respectively. In them, MTCNN was first used to detect faces in an image, and then the cropped face images were used to train the MobileNet model to determine whether there is a mask in the proper part of a face, based on the self-built datasets.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A swift and efficient method for face mask recognition based on off-the-shelf models

Qu,

Zheng,

Zhang

et al. 2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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Since the outbreak of COVID-19, the widespread use of face masks has prompted significant research efforts in face mask recognition. Based on the previous work, it is crucial to develop a face mask recognition system swiftly using offthe-shelf techniques, while it is equally important to ensure that the system's performance and efficiency meet the requirements of real-life scenarios. In this paper, we propose a two-fold face mask recognition method, by first detecting faces in an image using YuNet and then recognizing whether the faces are wearing a mask or not using MobileNet. After validation on a self-built dataset, compared to similar related works, our method can effectively improve the face detection rate, even in situations of occlusion and blurring, and the detection and recognition efficiency has been significantly improved too.

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Section: Experiments Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A swift and efficient method for face mask recognition based on off-the-shelf models

Qu,

Zheng,

Zhang

et al. 2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

View full text Add to dashboard Cite

show abstract

“…Following that, depth-wise convolution filters the features as the source of the ReLU6 section. In the third layer, the 1x1 conv layer is used to project data with a high number of dimensions into a tensor with a lower number of dimensions known as the bottleneck layer [8][9][10] [11].…”

Section: Introductionmentioning

confidence: 99%

Drowsy Eyes and Face Mask Detection for Car Drivers using the Embedded System

Budiarti,

Nugroho,

Ayunda

et al. 2023

regist. j. ilm. teknol. sist. inf.

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Efforts to prevent the spread of the COVID-19 virus have underscored the critical importance of mask-wearing as a preventive measure. Concurrently, road traffic accidents, often resulting from human error, have emerged as a significant contributor to global mortality rates. This study endeavors to address these pressing issues by employing advanced Deep Learning techniques to detect mask usage and identify drowsy eyes, thus contributing to the prevention of COVID-19 and accidents due to driver fatigue. To achieve this objective, an embedded system was developed, utilizing the integration of hardware and software components. The system effectively utilizes MobileNetV2 for face mask detection and employs HOG and SVM algorithms for drowsy eye detection. By seamlessly integrating these detection systems into a single embedded device, the simultaneous detection of both mask usage and drowsy eyes is made possible. The results demonstrates a commendable accuracy rate of 80% for face mask detection and 75% for drowsy eye detection. Furthermore, the mask detection component exhibits a remarkable training accuracy of 99%, while the drowsy eye detection component demonstrates an 80% training accuracy, affirming the system's efficacy in precisely identifying masks and drowsy eyes. The proposed embedded system offers potential applications in enhancing road safety. Its capability to effectively detect drowsy eyes and mask usage in car drivers contributes significantly to preventing accidents due to driver fatigue. Additionally, it plays a vital role in mitigating COVID-19 transmission by promoting widespread mask-wearing among individuals. This study exemplifies the potential of integrating Deep Learning methodologies with embedded systems, thus paving the way for future research and development in the realm of driver safety and virus prevention.

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