Masked-face recognition using deep metric learning and FaceMaskNet-21

Golwalkar, Rucha; Mehendale, Ninad

doi:10.1007/s10489-021-03150-3

Cited by 34 publications

(10 citation statements)

References 32 publications

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“…A system to identify students and track their bibliometric attendance was also developed by [61] to support educational platforms; however, the accuracy of that system was only 95.38% compared to our system's 99.38%. In addition, our finding was closely related to the result previously reported by [81], in which the authors show testing accuracy of 88.92% with a performance time of fewer than 10 ms. This system was found to be suitable for identifying individuals in CCTV footage in locations because it masked facial recognition in real-time.…”

Section: Resultssupporting

confidence: 91%

Facial Recognition System to Detect Student Emotions and Cheating in Distance Learning

et al. 2022

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Distance learning has spread nowadays on a large scale across the world, which has led to many challenges in education such as invigilation and learning coordination. These challenges have attracted the attention of many researchers aiming at providing high quality and credibility monitoring of students. Distance learning has offered an effective education alternative to traditional learning in higher education. The lecturers in universities face difficulties in understanding students’ emotions and abnormal behaviors during educational sessions and e-exams. The purpose of this study is to use computer vision algorithms and deep learning algorithms to develop a new system that supports lecturers in monitoring and managing students during online learning sessions and e-exams. To achieve the proposed objective, the system employs software methods, computer vision algorithms, and deep learning algorithms. Semi-structural interviews were also used as feedback to enhance the system. The findings showed that the system achieved high accuracy for student identification in real time, student follow-up during the online session, and cheating detection. Future work can focus on developing additional tools to assist students with special needs and speech recognition to improve the follow-up facial recognition system’s ability to detect cheating during e-exams in distance learning.

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Section: Resultssupporting

confidence: 91%

Facial Recognition System to Detect Student Emotions and Cheating in Distance Learning

et al. 2022

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“…In ref. [ 33 ], deep metric learning and the FaceMaskNet-21 architecture are employed for masked face recognition. They leverage the power of deep learning to learn discriminative features from masked faces and utilize a metric learning framework for enhanced recognition performance.…”

Section: Related Workmentioning

confidence: 99%

Innovative Hybrid Approach for Masked Face Recognition Using Pretrained Mask Detection and Segmentation, Robust PCA, and KNN Classifier

Eman

Mahmoud

Ibrahim

et al. 2023

Sensors

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Face masks are widely used in various industries and jobs, such as healthcare, food service, construction, manufacturing, retail, hospitality, transportation, education, and public safety. Masked face recognition is essential to accurately identify and authenticate individuals wearing masks. Masked face recognition has emerged as a vital technology to address this problem and enable accurate identification and authentication in masked scenarios. In this paper, we propose a novel method that utilizes a combination of deep-learning-based mask detection, landmark and oval face detection, and robust principal component analysis (RPCA) for masked face recognition. Specifically, we use pretrained ssd-MobileNetV2 for detecting the presence and location of masks on a face and employ landmark and oval face detection to identify key facial features. The proposed method also utilizes RPCA to separate occluded and non-occluded components of an image, making it more reliable in identifying faces with masks. To optimize the performance of our proposed method, we use particle swarm optimization (PSO) to optimize both the KNN features and the number of k for KNN. Experimental results demonstrate that our proposed method outperforms existing methods in terms of accuracy and robustness to occlusion. Our proposed method achieves a recognition rate of 97%, which is significantly higher than the state-of-the-art methods. Our proposed method represents a significant improvement over existing methods for masked face recognition, providing high accuracy and robustness to occlusion.

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“…Golwalkar et al ( 63 ) proposed a solution that uses deep metric learning and their own FacemaskNet-21 deep learning network to build 128-d encodings for face recognition in static pictures, live video streams, and static video files. With an execution time of less than 10 milliseconds, they were able to obtain a testing accuracy of 88.92%.…”

Section: State Of the Art Modelsmentioning

confidence: 99%

“…The occluded local parts can be found using handcrafted low-level features and they are excluded from the recognition process. Holistic learning approaches, local features, and shallow learning approaches are all described by LBPs ( 63 ), PCAs ( 66 ), and HOGs ( 78 ). Face recognition has been achieved using non-occluded tasks that are robust and accurate against many face changes, such as illumination, affine, rotation, scale, and translation.…”

Section: State Of the Art Modelsmentioning

confidence: 99%

Deep learning techniques for detecting and recognizing face masks: A survey

Alturki

Alharbi

AlAnzi

et al. 2022

Front. Public Health

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The year 2020 brought many changes to the lives of people all over the world with the outbreak of COVID-19; we saw lockdowns for months and deaths of many individuals, which set the world economy back miles. As research was conducted to create vaccines and cures that would eradicate the virus, precautionary measures were imposed on people to help reduce the spread the disease. These measures included washing of hands, appropriate distancing in social gatherings and wearing of masks to cover the face and nose. But due to human error, most people failed to adhere to this face mask rule and this could be monitored using artificial intelligence. In this work, we carried out a survey on Masked Face Recognition (MFR) and Occluded Face Recognition (OFR) deep learning techniques used to detect whether a face mask was being worn. The major problem faced by these models is that people often wear face masks incorrectly, either not covering the nose or mouth, which is equivalent to not wearing it at all. The deep learning algorithms detected the covered features on the face to ensure that the correct parts of the face were covered and had amazingly effective results.

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Masked-face recognition using deep metric learning and FaceMaskNet-21

Cited by 34 publications

References 32 publications

Facial Recognition System to Detect Student Emotions and Cheating in Distance Learning

Facial Recognition System to Detect Student Emotions and Cheating in Distance Learning

Innovative Hybrid Approach for Masked Face Recognition Using Pretrained Mask Detection and Segmentation, Robust PCA, and KNN Classifier

Deep learning techniques for detecting and recognizing face masks: A survey

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