Efficient Masked Face Recognition Method during the COVID-19 Pandemic

Hariri, Walid

doi:10.21203/rs.3.rs-39289/v1

Cited by 24 publications

(12 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hariri et al [12] suggested a technique of deep learning as well as a quantization-based procedure for identification of masked faces. The proposed algorithm may also be widened to better application areas like violence video retrieval and video surveillance.…”

Section: Related Workmentioning

confidence: 99%

Deep Neural Architecture for Face mask Detection on Simulated Masked Face Dataset against Covid-19 Pandemic

Negi

Kumar

Chauhan

et al. 2021

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

View full text Add to dashboard Cite

The dangerous COVID-19 (SARS-CoV-2) is rising steadily and globally, with more than 72,851,747 confirmed cases observed to WHO including 1,643,339 deaths till 17 December 2020. The country's economy is now almost fully halted, people are stuck up and investment becomes deteriorating. So, this is turning to worry of the government for a development and health. Health organizations are often desperate for evolving decision-making innovations to overcome this viral virus and encourage people to receive rapid and effective responses in realtime. Thus, it is important to create auto-mechanisms as a preventive shield to ensure healthy humanity against SARS-CoV-2. Advanced analytics methods and other strategies could also empower researchers, learners and the pharmaceutical industry to acknowledge the hazardous COVID-19 and speed it up care procedures by efficiently testing vast volumes of research data. The prevention method consequence is being used to effectively manage, calculate, forecast and monitor current infected people and future potential cases. Therefore, we proposed CNN and VGG16 based deep learning models to incorporate and enforce AI-based precautionary measures to detect the face mask on Simulated Masked Face Dataset (SMFD). This technique is capable of recognizing masked and unmasked faces to help monitor safety breaches, facilitate the use of face masks, and maintain a secure working atmosphere.

show abstract

Section: Related Workmentioning

confidence: 99%

Deep Neural Architecture for Face mask Detection on Simulated Masked Face Dataset against Covid-19 Pandemic

Negi

Kumar

Chauhan

et al. 2021

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

View full text Add to dashboard Cite

show abstract

“…Hariri developed a system that performs masked face recognition whereby occluded regions of the face are discarded [10]. Firstly, face detection is performed on the image followed by face alignment.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, in this work, we saw that the accuracy drops by an average of 5.6% when face masks are used. Hariri developed a system whereby only the upper half face is used and the accuracy achieved was 91.3% using the RMFRD dataset which consists of 525 subjects [10]. Our proposed system was tested with the same type of dataset but consisting of only 170 subjects and the highest accuracy achieved was 95.3%.…”

Section: G Comparison With Existing Workmentioning

confidence: 99%

“…Our proposed system was tested with the same type of dataset but consisting of only 170 subjects and the highest accuracy achieved was 95.3%. Hariri had used a VGG16 model while our best system requires MobileNetV2 [10]. The system developed by Anwar and Raychowdhury was tested using a dataset containing 42 images per person while our model was tested with 60 images per person [11].…”

Section: G Comparison With Existing Workmentioning

confidence: 99%

See 1 more Smart Citation

Is Face Recognition with Masks Possible?

Saib¹,

Pudaruth²

2021

IJACSA

View full text Add to dashboard Cite

With the recent outbreak of the COVID-19 pandemic, wearing face masks has become extremely important to protect us, and to reduce the spread of the virus. This measure has made many existing face recognition systems ineffective as they were trained to work with unmasked faces. In this paper, several methods have been proposed for masked face recognition. Two pre-trained deep learning architectures (VGG16, and MobileNetV2) and the Histogram of Gradients (HOG) technique were used to extract the relevant features from face images of celebrities. A SoftMax layer and Support Vector Machines (SVM) were used for classification. Five scenarios were devised to assess the different models and approaches. With an accuracy of 96.8%, the best model was obtained with MobileNetV2 with a SoftMax layer on the dataset consisting of a mixture of masked and unmasked images. Three different types of masks were also used in this study. The mean accuracy was 91.35% when the same type of mask is used for training and testing. However, the accuracy dropped by an average of 5.6% when a different type of mask is used for training and testing. A contactless attendance system using the best masked face recognition model has also been implemented.

show abstract

“…Face recognition also has a high rate of FTA due to facial accessories, such as mask, sunglasses, etc. Though recent research has been trying to solve the current problem with mask wearing by cropping and verifying the top part of the face, this can reduce some important attributes in face recognition, such as the nose and mouth, and increases the risk and simplicity of theft [25,26]. Gait can also easily affected by the clothes that the user wear, as well as the surface for the gait to be performed on [27].…”

Section: Comparison With Other Biometrics Modalitiesmentioning

confidence: 99%

Robustness of Rhythmic-Based Dynamic Hand Gesture with Surface Electromyography (sEMG) for Authentication

2020

View full text Add to dashboard Cite

Authentication has three basic factors—knowledge, ownership, and inherence. Biometrics is considered as the inherence factor and is widely used for authentication due to its conveniences. Biometrics consists of static biometrics (physical characteristics) and dynamic biometrics (behavioral). There is a trade-off between robustness and security. Static biometrics, such as fingerprint and face recognition, are often reliable as they are known to be more robust, but once stolen, it is difficult to reset. On the other hand, dynamic biometrics are usually considered to be more secure due to the constant changes in behavior but at the cost of robustness. In this paper, we proposed a multi-factor authentication—rhythmic-based dynamic hand gesture, where the rhythmic pattern is the knowledge factor and the gesture behavior is the inherence factor, and we evaluate the robustness of the proposed method. Our proposal can be easily applied with other input methods because rhythmic pattern can be observed, such as during typing. It is also expected to improve the robustness of the gesture behavior as the rhythmic pattern acts as a symbolic cue for the gesture. The results shown that our method is able to authenticate a genuine user at the highest accuracy of 0.9301 ± 0.0280 and, also, when being mimicked by impostors, the false acceptance rate (FAR) is as low as 0.1038 ± 0.0179.

show abstract

Efficient Masked Face Recognition Method during the COVID-19 Pandemic

Cited by 24 publications

References 29 publications

Deep Neural Architecture for Face mask Detection on Simulated Masked Face Dataset against Covid-19 Pandemic

Deep Neural Architecture for Face mask Detection on Simulated Masked Face Dataset against Covid-19 Pandemic

Is Face Recognition with Masks Possible?

Robustness of Rhythmic-Based Dynamic Hand Gesture with Surface Electromyography (sEMG) for Authentication

Contact Info

Product

Resources

About