Masked Face Detection using Multi-Graph Convolutional Networks

Alguzo, Alanoud; Alzu’bi, Ahmad; Albalas, Firas

doi:10.1109/icics52457.2021.9464553

Cited by 14 publications

(5 citation statements)

References 24 publications

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“…Face recognition [152] Skin lesion segmentation from dermoscopic images [153] Learning to cluster faces [154] Facial expression recognition method for identifying and recording emotion [155] Occluded face detection [156] Face anonymization with pose preservation [157] Consumer afect recognition using thermal facial ROIs [158] Criminal person recognition [159] Facial action unit detection [160] Masked face detection [161] Drunkenness face detection [162] Face detection and recognition [163] Driver drowsiness detection [164] Large-scale face clustering [165] Detection of facial action units [166] Facial expression recognition Action and activity recognition [167] Multiactor activity detection [168] One-shot video graph generation [169] Online graph depictions for tracking multiple 3D objects [170] Event stream classifcation [171] LiDAR-based 3D video object detection [172] Salient superpixel visual tracking [173] Video event recognition and elaboration from the bottom up [174] Multiobject tracking with embedded particle fow [175] Video scene graph generation [176] Video action detection [177] Multiobject tracking in autodriving [178,179] Skeleton-based action recognition [180] Video distinct object recognition by extraction of robust seeds [181] Video saliency detection [182] Close-to-real-time tracking in congested scenes Human pose detection [183] Human-object interaction detection [184] Railway driver behavior recognition system [185] Framework for object identifcation based on human local attributes…”

Section: Employmentmentioning

confidence: 99%

A State-of-the-Art Computer Vision Adopting Non-Euclidean Deep-Learning Models

Chowdhury,

Sany,

Ahamed

et al. 2023

International Journal of Intelligent Systems

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A distance metric known as non-Euclidean distance deviates from the laws of Euclidean geometry, which is the geometry that governs most physical spaces. It is utilized when Euclidean distance is inappropriate, for as when dealing with curved surfaces or spaces with complex topologies. The ability to apply deep learning techniques to non-Euclidean domains including graphs, manifolds, and point clouds is made possible by non-Euclidean deep learning. The use of non-Euclidean deep learning is rapidly expanding to study real-world datasets that are intrinsically non-Euclidean. Over the years, numerous novel techniques have been introduced, each with its benefits and drawbacks. This paper provides a categorized archive of non-Euclidean approaches used in computer vision up to this point. It starts by outlining the context, pertinent information, and the development of the field’s history. Modern state-of-the-art methods have been described briefly and categorized by application fields. It also highlights the model’s shortcomings in tables and graphs and shows different real-world applicability. Overall, this work contributes to a collective information and performance comparison that will help enhance non-Euclidean deep-learning research and development in the future.

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Section: Employmentmentioning

confidence: 99%

A State-of-the-Art Computer Vision Adopting Non-Euclidean Deep-Learning Models

Chowdhury,

Sany,

Ahamed

et al. 2023

International Journal of Intelligent Systems

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“…The RWMFD dataset is also replaced with two alternative datasets, MAFA and Human Faces, which are more sufficient in terms of size and diversity. Additionally, the work in [55] only determines whether the detected face is covered by a mask or not, while in this paper we perform both the binary OFD and multi-category OFD (i.e., unmasked, masked and occluded).…”

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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“…The same parameters were fine-tuned using transfer learning. The MobileNetV2 [17] classifier ADAM optimizer [11] is used to analysed the experimental results of system performance. After that, save the model.…”

Section: Train and Test The Modelmentioning

confidence: 99%

Live Streaming of Face Mask and Body Temperature Detection System using Transfer Learning and IoT

Singh

Tripathi

2022

J. Phys.: Conf. Ser.

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Since the outbreak of COVID-19 pandemic, enormous development has been done in computer vision systems in face masks and temperature detection. To enforce the mandate for wearing the mask in public places, the authors have suggested a solution using transfer learning (MobileNetV2) architecture as a foundation for image classification. The proposed solution is embedded devices with Raspberry Pi4 and python packages viz TensorFlow, OpenCV, and Keras. In this Work, a combination of transfer learning and IoT has been worked out using live streaming to detect an appropriate use of face masks along with body temperature. This technique includes the Single Shot Single box and Multi-box identification of the face mask. Further, a person can check the Temperature at the entrance. This temperature sensor is in noncontact mode, which senses a temperature from 2 cm to 5 cm and is displayed through LCD. It sends the data to the cloud for visualization and analysis, and an alert message is sent in a real-time environment based on that analysis.

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Masked Face Detection using Multi-Graph Convolutional Networks

Cited by 14 publications

References 24 publications

A State-of-the-Art Computer Vision Adopting Non-Euclidean Deep-Learning Models

A State-of-the-Art Computer Vision Adopting Non-Euclidean Deep-Learning Models

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

Live Streaming of Face Mask and Body Temperature Detection System using Transfer Learning and IoT

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