Towards Mask-robust Face Recognition

Feng, Tao; Xu, Lijuan; Hangjie, Yuan,; Zhao, Yongfei; Tang, Mingqian; Wang, Mang

doi:10.1109/iccvw54120.2021.00173

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…Due to its greater adaptability and resistance to face masks, the paired loss-based technique on the WebFace42M dataset obtained an All-MFR rate of 14.38%. For integrating the mask into the facial image and controlling noisy training datasets, Tao et al [40] suggested a UV-texture-mapping-based method as well as a self-learning-based cleaning procedure. The incorporation of the Balanced Curricular Loss (BCL), in addition to clever calculations that take the impact of long-tail distribution and challenging facial samples into account, has resulted in impressive experimental findings.…”

Section: Mask Robust Methodsmentioning

confidence: 99%

A Survey on Masked Face Recognition Amid the COVID-19 Pandemic

Zhong

2023

Frontiers in Artificial Intelligence and Applications

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Against the backdrop of the COVID-19 pandemic, the ubiquitous adoption of masks has caused significant hurdles to conventional face recognition technologies and their applications. In order to address these problems, a great deal of research related to masked face recognition has been put forward. Although masked face recognition has made great progress, the review or summary about this field is scanty. Therefore, this paper proposed a survey on masked face recognition. Firstly, the datasets of MFR and the mainstream deep learning methods are introduced. In addition, some common evaluation metrics are explained. Finally, this paper discusses the potential challenges of MFR and make conclusion.

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Section: Mask Robust Methodsmentioning

confidence: 99%

A Survey on Masked Face Recognition Amid the COVID-19 Pandemic

Zhong

2023

Frontiers in Artificial Intelligence and Applications

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“…The Re-ID community mainly focuses on two subtasks: image-based Re-ID [7][8][9][10] and video-based Re-ID. [11][12][13][14][15] Early methods prefer to learn discriminative features from isolated images or disjoint frames.…”

Section: Person Re-identificationmentioning

confidence: 99%

Learning to cluster person via graph convolution networks for video‐based person re‐identification

Li,

Feng,

et al. 2024

Concurrency and Computation

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SummaryUnsupervised person re‐identification based on video sequences can be applied to surveillance systems and is attracting much more attention. It aims to spot specific person in other scenes captured by different cameras. This work explores an innovative strategy, namely, learning to cluster unlabeled person in the videos through graph convolutional networks. In this article, we find that the possibility of inter‐frame linkage can be inferred from context. Therefore, a pose‐guided topology linkage clustering framework is proposed. Our framework consists of three modules: (i) a pose‐guided representation module; (ii) a pose‐guided embedding module; (iii) a link prediction module. First, the representation coding alone is performed at the level of relational induction bias, embedding the implicit pose structure information in image features. Then, based on the consideration of the topology relationship between adjacent and cross‐frame, graph convolutional network is introduced to infer the likelihood of linkage between frame nodes. Experiments show that the proposed method demonstrates excellent scalability in addition to being an effective response to person clustering in case of changes, and does not need the number of clusters as a prior.

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“…Karasugi etal. [22] and Feng et al [23] proposed a mask-enhanced facial recognition system. Using different loss functions in deep learning, mask recognition and facial recognition under the mask can be achieved.…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Physiological Measurement System for Wearing Masks During the Epidemic

Chiang¹,

Wu²

2023

Computers, Materials &Amp; Continua

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Physiological signals indicate a person's physical and mental state at any given time. Accordingly, many studies extract physiological signals from the human body with non-contact methods, and most of them require facial feature points. However, under COVID-19, wearing a mask has become a must in many places, so how non-contact physiological information measurements can still be performed correctly even when a mask covers the facial information has become a focus of research. In this study, RGB and thermal infrared cameras were used to execute non-contact physiological information measurement systems for heart rate, blood pressure, respiratory rate, and forehead temperature for people wearing masks due to the pandemic. Using the green (G) minus red (R) signal in the RGB image, the region of interest (ROI) is established in the forehead and nose bridge regions. The photoplethysmography (PPG) waveforms of the two regions are obtained after the acquired PPG signal is subjected to the optical flow method, baseline drift calibration, normalization, and bandpass filtering. The relevant parameters in Deep Neural Networks (DNN) for the regression model can correctly predict the heartbeat and blood pressure. In addition, the temperature change in the ROI of the mask after thermal image processing and filtering can be used to correctly determine the number of breaths. Meanwhile, the thermal image can be used to read the temperature average of the ROI of the forehead, and the forehead temperature can be obtained smoothly. The experimental results show that the above-mentioned physiological signals of a subject can be obtained in 6-s images with the error for both heart rate and blood pressure within 2%∼3% and the error of forehead temperature within ±0.5°C.

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Towards Mask-robust Face Recognition

Cited by 9 publications

References 9 publications

A Survey on Masked Face Recognition Amid the COVID-19 Pandemic

A Survey on Masked Face Recognition Amid the COVID-19 Pandemic

Learning to cluster person via graph convolution networks for video‐based person re‐identification

Non-Contact Physiological Measurement System for Wearing Masks During the Epidemic

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