Facial Expression Recognition with the advent of face masks

Yang, Bo; Wu, Jianming; Hattori, Gen

doi:10.1145/3428361.3432075

Cited by 30 publications

(38 citation statements)

References 5 publications

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“…In addition, there are many other techniques [34]- [38] developed for face and WMD. Accurate locations of facial masks can improve the accuracy of face recognition algorithms [39]- [43]. In this article, our main concern is WMD, as shown in Fig.…”

Section: A Facial Mask Detection Methodsmentioning

confidence: 99%

Hybrid Transfer Learning and Broad Learning System for Wearing Mask Detection in the COVID-19 Era

Wang

Zhao

Chen

2021

IEEE Trans. Instrum. Meas.

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In the era of Corona Virus Disease 2019 (COVID-19), wearing a mask can effectively protect people from infection risk and largely decrease the spread in public places, such as hospitals and airports. This brings a demand for the monitoring instruments that are required to detect people who are wearing masks. However, this is not the objective of existing face detection algorithms. In this article, we propose a two-stage approach to detect wearing masks using hybrid machine learning techniques. The first stage is designed to detect candidate wearing mask regions as many as possible, which is based on the transfer model of Faster_RCNN and InceptionV2 structure, while the second stage is designed to verify the real facial masks using a broad learning system. It is implemented by training a two-class model. Moreover, this article proposes a data set for wearing mask detection (WMD) that includes 7804 realistic images. The data set has 26403 wearing masks and covers multiple scenes, which is available at "https://github.com/BingshuCV/WMD." Experiments conducted on the data set demonstrate that the proposed approach achieves an overall accuracy of 97.32% for simple scene and an overall accuracy of 91.13% for the complex scene, outperforming the compared methods. Index Terms-Broad learning system (BLS), Corona Virus Disease 2019 (COVID-19), transfer learning, wearing mask detection (WMD). I. INTRODUCTION S INCE the first patient infected by Corona Virus Disease 2019 (COVID-19) has been identified in 2019, the virus spread the world very fast. It is quickly declared as a global Manuscript

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Section: A Facial Mask Detection Methodsmentioning

confidence: 99%

Hybrid Transfer Learning and Broad Learning System for Wearing Mask Detection in the COVID-19 Era

Wang

Zhao

Chen

2021

IEEE Trans. Instrum. Meas.

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“…The results generated by masked facial detection methods can be sent to face recognition model to implement the identification verification [65]. Masked face expression recognition [137] is also an interesting application. Masked faces can be used for unmask or face restoration [138], which is promising in the field of safety protection.…”

Section: E Discussion On the Results Of Methodsmentioning

confidence: 99%

“…Non-contact temperature measurement is shown in second row, which comes from "https://gongyi.gmw.cn/2020-03/23/content 33675137.htm". The images in last row from left to right are collected from [65], [137], [138], respectively.…”

Section: Masked Face Recognitionmentioning

confidence: 99%

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A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

Wang

Zheng

Chen

2022

IEEE Trans. Artif. Intell.

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“…However, all of the existing occlusion FER datasets are not specified for facemask-aware FER. To solve the problem of the lack of a FER dataset with face masks, we proposed an automatic wearing face mask (AWFM) approach in our previous work, which could automatically add face masks to existing FER datasets using differently shaped masks according to facial orientations [14]. We also evaluated the performance of two famous deep learning models (MobileNet [15] and VGG19 [16]) for facial emotion classification with three categories (positive, neutral, negative) on the masked FER dataset, which had already been released to GitHub under the name of M-LFW-FER.…”

Section: Partial Occlusion Fer Datasetmentioning

confidence: 99%

Face Mask Aware Robust Facial Expression Recognition During The Covid-19 Pandemic

Yang

Hattori

2021

2021 IEEE International Conference on Image Processing (ICIP)

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Wearing face masks is considered an effective means of preventing the transmission of coronavirus during the COVID-19 pandemic. Facial expression recognition (FER) under partial occlusion, especially with face masks, makes it a challenging task in the research area of computer vision. In this paper, we propose a two-stage attention model to improve the accuracy of face-mask-aware FER: In stage 1, we train the masked/unmasked binary deep classifier, which can generate attention heatmaps to roughly distinguish the masked facial parts from the unobstructed region. In stage 2, we train the FER classifier, which is guided to pay more attention to the region that is essential to the facial expression classification, and both occluded and non-occluded regions are taken into consideration but reweighed. The proposed method outperforms other state-of-the-art occlusion-aware FER methods on face-mask-aware FER datasets, whether in the wild or in the laboratory.

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Facial Expression Recognition with the advent of face masks

Cited by 30 publications

References 5 publications

Hybrid Transfer Learning and Broad Learning System for Wearing Mask Detection in the COVID-19 Era

Hybrid Transfer Learning and Broad Learning System for Wearing Mask Detection in the COVID-19 Era

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

Face Mask Aware Robust Facial Expression Recognition During The Covid-19 Pandemic

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