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Article
Computer Science, Artificial Intelligence
Mingjie He et al.
Summary: In this paper, a novel method for simulating occlusion by dropping the activations of a group of neurons is proposed, along with an attention module to improve the contributions of non-occluded regions. Experimental results show that the proposed method achieves significant improvements in the robustness and accuracy of face recognition.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Computer Science, Information Systems
Xueping Su et al.
Summary: Wearing masks is an important way to prevent the transmission of COVID-19, but detecting mask-wearing in the real world is challenging due to various factors. This study proposes a new algorithm for mask detection and classification that combines transfer learning and deep learning, showing superior performance in experiments compared to existing algorithms.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Multidisciplinary Sciences
Rubina Sarki et al.
Summary: This study aims to develop a deep learning-based system for the classification and detection of COVID-19 using chest radiography. Various state-of-the-art convolutional neural networks (CNNs) are evaluated for medical image classification using a public X-ray dataset. The results show high classification accuracy for both transfer learning and training from scratch.
Article
Engineering, Electrical & Electronic
Walid Hariri
Summary: This paper proposes a reliable method for masked face recognition by removing occlusions, extracting deep features, and using Multilayer Perceptron (MLP) for classification, which shows high recognition performance on a real-world dataset compared to other state-of-the-art methods.
SIGNAL IMAGE AND VIDEO PROCESSING
(2022)
Article
Environmental Sciences
Emrah Aydemir et al.
Summary: The study developed a high-accuracy model for detecting appropriate face mask use by collecting and classifying images, utilizing deep features and a support vector machine classifier.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Article
Computer Science, Artificial Intelligence
Hoai Nam Vu et al.
Summary: This paper proposes a method that combines deep learning and Local Binary Pattern features to recognize masked faces using RetinaFace as an efficient encoder. Experimental results show that this method outperforms other face recognition methods.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Rucha Golwalkar et al.
Summary: Due to the pandemic of COVID-19, wearing facial masks has become mandatory worldwide to prevent the spread of the virus. However, conventional face recognition systems are no longer effective as they cannot recognize individuals whose important facial features are covered by masks. In this study, a system utilizing deep metric learning technique and FaceMaskNet-21 deep learning network is proposed to generate 128-d encodings for face recognition even with masks on.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Fadi Boutros et al.
Summary: This paper presents a solution to improve masked face recognition performance by introducing the Embedding Unmasking Model (EUM) and the Self-restrained Triplet (SRT) loss function. Experimental results demonstrate that the proposed approach significantly enhances performance in various settings.
PATTERN RECOGNITION
(2022)
Article
Engineering, Multidisciplinary
Mohamed Loey et al.
Summary: This paper introduces a hybrid model using deep and classical machine learning for face mask detection. The model consists of two components for feature extraction and mask classification, achieving high testing accuracy in different datasets.
Article
Computer Science, Artificial Intelligence
Yande Li et al.
Summary: The research introduces a new method for masked face recognition, which significantly improves performance by integrating a cropping-based approach with the Convolutional Block Attention Module.
APPLIED INTELLIGENCE
(2021)
Article
Chemistry, Multidisciplinary
Borut Batagelj et al.
Summary: The COVID-19 pandemic has spurred research into face-mask detection techniques, with a focus on evaluating existing face detectors, detecting proper mask placement, and assessing the usefulness of these techniques in monitoring applications. This study aims to answer questions regarding the performance of face detectors with masked-face images, the detection of proper mask placement, and the effectiveness of existing face-mask detection techniques during the pandemic.
APPLIED SCIENCES-BASEL
(2021)
Article
Construction & Building Technology
Mohamed Loey et al.
Summary: Deep learning models have shown potential in object detection, particularly in identifying medical face masks for COVID-19 protection. The proposed model in this research achieved higher accuracy and precision compared to related works, with an adam optimizer reaching 81% average precision percentage.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Construction & Building Technology
Imran Ahmed et al.
Summary: The COVID-19 outbreak has caused a global disaster, increasing population vulnerability due to the lack of effective remedies and vaccines. Social distancing is considered a key precaution, while a deep learning platform is used for tracking and detecting individuals to prevent virus spread by enforcing social distance regulations.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Engineering, Electrical & Electronic
Yuzhen Chen et al.
Summary: The COVID-19 pandemic has caused significant global impact, with high numbers of confirmed cases and deaths. Wearing masks is a key measure to curb the spread of the virus, and a mobile phone-based detection system for mask usage has been developed to improve mask-wearing efficiency.
IEEE SENSORS JOURNAL
(2021)
Article
Chemistry, Analytical
Jimin Yu et al.
Summary: This paper proposes a facial mask recognition algorithm based on the improved YOLO-v4, which introduces improved network structures and dataset preparation methods to enhance model accuracy and real-time performance. Evaluation of the deep learning algorithm indicates significant improvements in the effectiveness of the proposed algorithm.
Article
Computer Science, Artificial Intelligence
Dan Zeng et al.
Summary: This article addresses the issue of face recognition under occlusions, presenting a systematic categorization of existing methods and discussing future challenges and trends in occluded face recognition. By analyzing how current face recognition methods handle occlusion, the authors classify them into three categories and assess the motivation, innovation, pros and cons, and performance of representative approaches for comparison. Ultimately, the study provides insight into the importance and implications of occluded face recognition.
Article
Computer Science, Information Systems
Xinbei Jiang et al.
Summary: The rapid outbreak of COVID-19 has led to a growing demand for automatic real-time mask detection services. This paper proposed the Properly Wearing Masked Face Detection Dataset (PWMFD) and the SE-YOLOv3 mask detector, which outperformed other detectors on PWMFD according to experimental results.
Article
Chemistry, Analytical
Dongfang Yang et al.
Summary: This study proposes a vision-based real-time monitoring system for detecting social distancing violations and slowing the spread of COVID-19 through warnings. It also introduces a critical social density value to keep the chance of violations near zero. The system does not record data, target individuals, or require human supervision during operation, and has been evaluated using real-world datasets.
Article
Chemistry, Multidisciplinary
Hongxia Deng et al.
Summary: In order to improve facial recognition accuracy for mask wearers during the COVID-19 pandemic, this paper proposes a masked-face recognition algorithm based on large margin cosine loss (MFCosface). By generating masked-face images through key facial feature detection and mask template selection, the algorithm greatly enhances the accuracy of masked-face recognition on several datasets.
APPLIED SCIENCES-BASEL
(2021)
Proceedings Paper
Computer Science, Cybernetics
Xinqi Fan et al.
Summary: This paper introduces RetinaFaceMask, a high-performance single-stage face mask detector, with a new dataset containing correct and incorrect mask-wearing states annotations, a context attention module, and knowledge transfer strategy from face detection task. Experimental results show the superiority of the proposed model on both existing and new datasets.
2021 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
(2021)
Article
Engineering, Electrical & Electronic
Bingshu Wang et al.
Summary: In the era of COVID-19, wearing masks can effectively reduce infection risk and spread in public places. However, existing face detection algorithms do not focus on detecting wearing masks. This article proposes a two-stage approach using hybrid machine learning techniques to detect wearing masks, achieving high accuracy on a dataset containing realistic images.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Health Care Sciences & Services
Jesus Tomas et al.
Summary: The COVID-19 pandemic has had a profound impact globally, highlighting the importance of correct facemask usage as a key preventive measure. Artificial Intelligence and facial recognition techniques can be utilized to detect facemask misuse and reduce virus transmission effectively, with a proposed intelligent method in this study.
Review
Computer Science, Information Systems
Afsana Nowrin et al.
Summary: The outbreak of Covid-19 has caused a significant impact on a global scale, highlighting the importance of wearing facemasks to prevent the spread of the virus. The development and utilization of facemask detection algorithms, particularly those based on deep learning, are crucial for ensuring public safety. Future research is needed to further enhance the efficiency of these algorithms in the context of Covid-19.
Article
Computer Science, Information Systems
Jun Zhang et al.
Summary: This study focuses on detecting the fine-grained wearing state of face mask and introduces a new dataset as well as Context-Attention R-CNN framework, achieving significant improvement by extracting distinguishing features.
Review
Engineering, Electrical & Electronic
Samadhi P. K. Wickrama Arachchilage et al.
EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING
(2020)
Article
Chemistry, Analytical
Bosheng Qin et al.
Article
Chemistry, Multidisciplinary
Mahdi Rezaei et al.
APPLIED SCIENCES-BASEL
(2020)
Proceedings Paper
Engineering, Electrical & Electronic
Suryadi Suryadi et al.
2020 INTERNATIONAL CONFERENCE ON RADAR, ANTENNA, MICROWAVE, ELECTRONICS, AND TELECOMMUNICATIONS (ICRAMET): FOSTERING INNOVATION THROUGH ICTS FOR SUSTAINABLE SMART SOCIETY
(2020)
Article
Computer Science, Artificial Intelligence
Cho Ying Wu et al.
PATTERN RECOGNITION
(2018)
Review
Computer Science, Information Systems
Song Zhou et al.
HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Weiyang Liu et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Gao Huang et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Shiming Ge et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
A. Nieto-Rodriguez et al.
PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2015)
(2015)
Article
Computer Science, Artificial Intelligence
YS Gao et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2002)
