A cascade framework for masked face detection

Bu, Wei; Xiao, Jiangjian; Zhou, Chuanhong; Yang, Minmin; Peng, Chengbin

doi:10.1109/iccis.2017.8274819

Cited by 82 publications

(25 citation statements)

References 15 publications

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“…where TP, TN, FP and FN denote the true positive, true Table I compares our proposed framework to the cascaded framework used in [12]. The higher accuracy of the cascaded framework is due to the fact that it was designed to work on images rather than videos.…”

Section: B Experimental Results and Statisticsmentioning

confidence: 99%

“…The higher accuracy of the cascaded framework is due to the fact that it was designed to work on images rather than videos. Also, the "MASKED FACE" dataset [12] which was used to test the cascaded framework comprises of people wearing head gear. On the other hand, the dataset used to evaluate our proposed framework captures the various types of face masks worn by the public as a precautionary measure for disease control.…”

Section: B Experimental Results and Statisticsmentioning

confidence: 99%

“…Bu et al [12] built a CNN-based cascaded face detector framework, consisting of three convolutional neural networks. The first CNN, Mask-1 is a very shallow fully convolutional layer network with 5 layers that gives a probability of being a masked face for each detection window, followed by a Nonmaximum Supression (NMS) to merge overlapping candidates.…”

Section: Related Work and Literaturementioning

confidence: 99%

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Deep Learning Framework to Detect Face Masks from Video Footage

Joshi

Kanahasabai

et al. 2020

2020 12th International Conference on Computational Intelligence and Communication Networks (CICN)

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The use of facial masks in public spaces has become a social obligation since the wake of the COVID-19 global pandemic and the identification of facial masks can be imperative to ensure public safety. Detection of facial masks in video footages is a challenging task primarily due to the fact that the masks themselves behave as occlusions to face detection algorithms due to the absence of facial landmarks in the masked regions. In this work, we propose an approach for detecting facial masks in videos using deep learning. The proposed framework capitalizes on the MTCNN face detection model to identify the faces and their corresponding facial landmarks present in the video frame. These facial images and cues are then processed by a neoteric classifier that utilises the MobileNetV2 architecture as an object detector for identifying masked regions. The proposed framework was tested on a dataset which is a collection of videos capturing the movement of people in public spaces while complying with COVID-19 safety protocols. The proposed methodology demonstrated its effectiveness in detecting facial masks by achieving high precision, recall, and accuracy.

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Section: B Experimental Results and Statisticsmentioning

confidence: 99%

Section: B Experimental Results and Statisticsmentioning

confidence: 99%

Section: Related Work and Literaturementioning

confidence: 99%

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Deep Learning Framework to Detect Face Masks from Video Footage

Joshi

Kanahasabai

et al. 2020

2020 12th International Conference on Computational Intelligence and Communication Networks (CICN)

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“…As can be seen from the analysis of existing developments in the field of medical mask recognition, most approaches are based on neural network methods [1,2,3]. The main difference between the proposed method and the existing ones is the use of pre-trained weights on faces to initialize the neural network before training, which significantly increases both the learning speed (up to 10 epochs) and the quality of the classifier.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Finally, there is also a comparative study for better understanding. In [3], the authors propose a new cascade structure based on convolutional neural networks, which consists of three carefully designed convolutional neural networks for detecting masked faces. The authors in their work talk about the applicability of the algorithm for tracking and identifying criminals or terrorists.…”

Section: Introductionmentioning

confidence: 99%

Definition of Unique Objects by Convolutional Neural Networks using Transfer Learning

D.E¹,

V.V²,

V.N³

2020

IJACSA

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This article solves the problem of detecting medical masks on a person's face. Medical mask is one of the most effective measures to prevent infection with COVID-19, and its automatic detection is an actual task. The introduction of automatic recognition of medical masks in existing information security systems will allow quickly identify the violator of the mask regime, which in turn will increase security in a pandemic. The article provides a detailed analysis of existing solutions for face detection and automatic recognition of medical masks, method based on the use of convolutional neural networks was proposed. A distinctive feature of the new method is the use of two neural networks at once, using the RetinaFace neural network architecture at the face search stage and using the Resnet neural network architecture at the face mask recognition stage. It is shown that the use of transfer learning on scales, learned to work with faces, significantly accelerates learning and increases the accuracy of recognition. However, with this approach, there are some false positives, for example, when you try to cover your face with your hands, imitating a medical mask. Based on the study, we can conclude that the algorithm is applicable in the security system to determine the presence/absence of a medical mask on a person's face, as well as the need for additional research to solve the problems of false positives of the algorithm.

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A Dynamic Neural Network Model for Accurate Recognition of Masked Faces

2024

Data Analysis and Related Applications 3

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A cascade framework for masked face detection

Cited by 82 publications

References 15 publications

Deep Learning Framework to Detect Face Masks from Video Footage

Deep Learning Framework to Detect Face Masks from Video Footage

Definition of Unique Objects by Convolutional Neural Networks using Transfer Learning

A Dynamic Neural Network Model for Accurate Recognition of Masked Faces

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