RetinaFaceMask: A Single Stage Face Mask Detector for Assisting Control of the COVID-19 Pandemic

Fan, Xinqi; Jiang, Mingjie

doi:10.48550/arxiv.2005.03950

Cited by 7 publications

(10 citation statements)

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“…The database has been separated into a train, validation and test set. The size of the input image is 840 to 840 and batch size 2 for the ResNet backbone; the size of the input image is 640 to 640 to 32 for the MobileNet backbone [27,28].…”

Section: Methodsmentioning

confidence: 99%

A robust facemask forgery detection system in video

Almukhtar¹

2022

PEN

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An in-depth fake video uses an Artificial Intelligent (AI), AI programming, and a Personal computer (PC) mix to create a deep fake video of the action. Deep-faking can also be used to represent images and sounds. We provide insights into our reviews in this document. We're showing our dataset to start. At this point, we present the subtleties and reproductively of exploratory settings to evaluate the discovered effects finally. It is no surprise to find deep fake videos, which only monitor a tiny section of the video (e.g., the target face appears quickly on the video; hence the time is limited). We remove our system's fixed duration's persistent effects as each video contributes to the preparation, approval, and testing sections to reflect this. The edge groups are isolated from each video successively (without outline skips). The entire pipeline is ready to be finished when the approval stage is ten years old. Convolutional Neural Network (CNN) was the best and most reliable of the classification systems. Fake videos typically use low-quality pictures to mask faults or insist that the general public regard camera defects as unexplainable phenomena. 'This is a common trope with Unidentified Flying Object (UFO) videos: ghostly orbs are lenses; snakes are compression artifacts on one's face. In this study, we have implemented a sophisticated, knowledgeable method to recognize false images. Our test results using various monitored videos have shown that we can reliably predict whether videos are monitored through with simple co-evolutionary Long Short-Term Memory (LSTM) structure.

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Section: Methodsmentioning

confidence: 99%

A robust facemask forgery detection system in video

Almukhtar¹

2022

PEN

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“…FaceNet has an inferencing model trained on the CASIA-WebFace dataset and achieves an accuracy of 99.05% on the LFW benchmark. According ArcFace [76] model, there exists evidence of racial bias in the CASIA-WebFace. It was found that Caucasian distribution has a margin that stands out from the other races implying a higher probability of recognition errors in non-Caucasian subjects.…”

Section: ) Casia-webfacementioning

confidence: 99%

“…RetinaFaceMask [76] is an open-source face detector that appeared in 2020 and is hosted on GitHub [139]. The source code is available in Python.…”

Section: ) Retinafacemaskmentioning

confidence: 99%

Open-Source Face Recognition Frameworks: A Review of the Landscape

Wanyonyi

Çelik

2022

IEEE Access

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From holistic low-dimension feature-based segmentation to deep polynomial neural networks, Face Recognition (FR) accuracy has increased dramatically since its early days. The advancement and maturity of open-source FR frameworks have contributed to this trend, influencing many open-source research publications available in the public domain. The availability of modern accelerated computing capabilities through Graphics Process Unit (GPU) technology has played a substantial role in advancing open-source FR capabilities. The evolution and success of the open-source DL algorithms on FR, leveraging GPU technologies, have benefited from open datasets, resulting in many FR open-source implementations. This paper reviews the landscape of open-source FR frameworks, covering components of the FR pipeline across open datasets, face detection, face alignment, face representation, identification and verification, and deployment environments. We also discuss the current challenges and emerging directions in FR research.

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“…Deep learning methods are among the most successful approaches that have been widely examined in the domain of non-mask face detection, including deep features [34], [35], face detection on social media [36], [37], and video-based face detection [38], [39]. Deep learning algorithms allow the artificial neural networks to learn the key facial points and their locations in a comprehensive way [40]. Many sophisticated approaches have adopted the unrestricted scenario for face detection techniques, due to the limitations caused by distortions, exaggerated expressions, or large obstructions, which also made the dataset used to process such scenarios so limited.…”

Section: Related Workmentioning

confidence: 99%

Learning Discriminant Spatial Features With Deep Graph-Based Convolutions for Occluded Face Detection

et al. 2022

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The use of face masks has become a widespread non-pharmaceutical practice to mitigate the transmission of COVID-19. However, achieving accurate facial detection while people wear masks or similar face occlusions is a major challenge. This paper introduces a model to detect occluded or masked faces based on fused convolutional graphs. This model includes a deep neural architecture with two spatial-based graphs that rely on a set of key facial features. First, a distance graph is used to identify geographical similarity between the facial nodes that represent certain key face parts. Second, a correlation graph is formulated to compute the correlations between every two nodes that represent two different augmented facial modalities. Transfer learning is then performed using a pretrained deep architecture as a baseline to map the abstract semantic information into multiple feature filters. Then, discriminant graph convolutions are constructed based on the fusion of distance and correlation graphs. This model evaluates two tasks of facial detection, which are the binary detection of masked or unmasked faces, and multi-category detection of masked, unmasked, or occluded face with no mask. The experimental results on two benchmarking real-world datasets show that the proposed deep learning model is highly effective with an accuracy of 98% achieved in binary detection. Even with high variance in image occlusions, our proposed model has great promise in detecting and distinguishing between types of facial occlusion with an accuracy of 86% reported in multi-category detection.

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RetinaFaceMask: A Single Stage Face Mask Detector for Assisting Control of the COVID-19 Pandemic

Cited by 7 publications

References 0 publications

A robust facemask forgery detection system in video

A robust facemask forgery detection system in video

Open-Source Face Recognition Frameworks: A Review of the Landscape

Learning Discriminant Spatial Features With Deep Graph-Based Convolutions for Occluded Face Detection

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