Masked Face Recognition Using Deep Metric Learning and FaceMaskNet-21

Golwalkar, Rucha; Mehendale, Ninad

doi:10.2139/ssrn.3731223

Cited by 9 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The purpose of this procedure is to identify the person in any expression. As proposed by Golwalkar and Mehendale [11], the system is designed to recognize the face of the person wearing the mask. Since it is mandatory to wear a mask in COVID-19 situations, half the face is hidden behind the mask, and it is difficult to identify the person and wearing masks has led to an increase in thefts and robberies.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning for Masked Face Recognition in COVID-19 Pandemic Situation

Fatima¹,

Ghauri²,

Mohammad³

et al. 2022

MMEP

View full text Add to dashboard Cite

In the current epidemic, the whole world is suffering with the infectious disease i.e., Corona Virus Disease (COVID-19). It is important to wear a mask to minimize the transmission of the disease. When everyone is wearing a face mask, it is difficult for recognition systems to recognize the masked face of a specific person. As some of the facial features are covered behind the mask e.g., mouth and nose. Therefore, the face-recognizing systems are inefficient to recognize the masked faces. To solve this issue, a face recognition system is proposed to recognize masked and unmasked faces. Support vector machine (SVM) and Random Forest (RF) based classifiers are trained on the specific dataset and classifiers effectively recognize the masked and unmasked faces. The classifier recognizes the human facial features such as eyes, eyebrows, forehead, ears, and hair. The dataset is collected in the form of images for 28 classes with and without a face mask. The trained system will recognize the person, whether the person is wearing a mask or not. The recognition accuracy is approximately 98.2% for different classes and the proposed recognizer is also compared with the state of art existing techniques.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning for Masked Face Recognition in COVID-19 Pandemic Situation

Fatima¹,

Ghauri²,

Mohammad³

et al. 2022

MMEP

View full text Add to dashboard Cite

show abstract

“…The Labeled Faces in the Wild (LFW) dataset [156] includes 50,000 images approximately. For training, Golwalkar et al [157] used masked faces of 13 people and 204 images. For testing, they used the same face images but with 25 images of each person.…”

Section: Standard Datasetsmentioning

confidence: 99%

“…Golwalkar et al [157] employed the FaceMaskNet-21 network trained using quadruplets with deep metric learning to immediately identify masked faces. The 128-d encodings were generated for every face in the dataset and the input image or live video stream.…”

Section: Masked Face Recognitionmentioning

confidence: 99%

Masked Face Recognition Using Deep Learning: A Review

et al. 2021

View full text Add to dashboard Cite

A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR.

show abstract

“…However, their results showed a large deviation in robustness for images taken from the side. Rucha et al [10] proposed the FaceMaskNet-21 deep learning network to extract 128-d codes from static images, real-time video streams, and video files to aid in facial recognition. HOG technology was used to rapidly identify faces wearing masks.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition With Masks Based on Spatial Fine-Grained Frequency Domain Broadening

Chen

Xie

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Along with social distancing, wearing masks is an effective method of preventing the transmission of COVID-19 in the ongoing pandemic. However, masks occlude a large number of facial features, preventing facial recognition. The recognition rate of existing methods may be significantly reduced by the presence of masks. In this paper, we propose a method to effectively solve the problem of the lack of facial feature information needed to perform facial recognition on people wearing masks. The proposed approach uses image super-resolution technology to perform image preprocessing along with a deep bilinear module to improve EfficientNet. It also combines feature enhancement with frequency domain broadening, fuses the spatial features and frequency domain features of the unoccluded areas of the face, and classifies the fused features. The features of the unoccluded area are increased to improve the accuracy of recognition of masked faces. The results of a cross-validation show that the proposed approach achieved an accuracy of 98% on the RMFRD dataset, as well as a higher recognition rate and faster speed than previous methods. In addition, we also performed an experimental evaluation in an actual facial recognition system and achieved an accuracy of 99%, which demonstrates the effectiveness and practicability of the proposed method.INDEX TERMS Face recognition with mask, convolutional neural network, frequency domain widening, bilinear module, RMFRD dataset.

show abstract

Masked Face Recognition Using Deep Metric Learning and FaceMaskNet-21

Cited by 9 publications

References 0 publications

Machine Learning for Masked Face Recognition in COVID-19 Pandemic Situation

Machine Learning for Masked Face Recognition in COVID-19 Pandemic Situation

Masked Face Recognition Using Deep Learning: A Review

Face Recognition With Masks Based on Spatial Fine-Grained Frequency Domain Broadening

Contact Info

Product

Resources

About