Masked Face Recognition Using Deep Learning: A Review

Alzu’bi, Ahmad; Albalas, Firas; Al-Hadhrami, Tawfik; Younis, Lojin Bani; Bashayreh, Amjad

doi:10.3390/electronics10212666

Cited by 42 publications

(23 citation statements)

References 157 publications

(258 reference statements)

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“…A convolutional neural network (CNN) is used for image processing [ 52 ] but in other circumstances, researchers prefer to work with TextCNN for text classification, which necessitates the addition of a word embedding layer and a one-dimensional convolutional network to the model’s original structure [ 53 ].…”

Section: The Mlid Approachmentioning

confidence: 99%

“…It is computed by adding Concordance Percentage and 0.5 times of Tied Percentage [ 181 ]. False-positive rate (FPR): is a reliable and valid instrument used to identify the ratio of incorrectly classified negative samples to the overall number of negative samples [ 52 ]. Specificity: is a metric used to assess the accuracy of negative instances [ 52 ].…”

Section: Standard Evaluation Metricsmentioning

confidence: 99%

“… False-positive rate (FPR): is a reliable and valid instrument used to identify the ratio of incorrectly classified negative samples to the overall number of negative samples [ 52 ]. Specificity: is a metric used to assess the accuracy of negative instances [ 52 ]. Miss Rate: It is the percentage of positive samples that are misclassified [ 100 ].…”

Section: Standard Evaluation Metricsmentioning

confidence: 99%

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A survey of uncover misleading and cyberbullying on social media for public health

et al. 2022

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Misleading health information is a critical phenomenon in our modern life due to advance in technology. In fact, social media facilitated the dissemination of information, and as a result, misinformation spread rapidly, cheaply, and successfully. Fake health information can have a significant effect on human behavior and attitudes. This survey presents the current works developed for misleading information detection (MLID) in health fields based on machine learning and deep learning techniques and introduces a detailed discussion of the main phases of the generic adopted approach for MLID. In addition, we highlight the benchmarking datasets and the most used metrics to evaluate the performance of MLID algorithms are discussed and finally, a deep investigation of the limitations and drawbacks of the current progressing technologies in various research directions is provided to help the researchers to use the most proper methods in this emerging task of MLID.

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Section: The Mlid Approachmentioning

confidence: 99%

Section: Standard Evaluation Metricsmentioning

confidence: 99%

Section: Standard Evaluation Metricsmentioning

confidence: 99%

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A survey of uncover misleading and cyberbullying on social media for public health

et al. 2022

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“…Several biometric-based authentication systems are shown in [9,43,[45][46][47]. Additionally, facial biometric authentication [48] plays a crucial part in electronic payment authentication. Built-in video cameras, particularly front-facing digital cameras, are now more common in mobile phones.…”

Section: Literature Reviewmentioning

confidence: 99%

Device Identity-Based User Authentication on Electronic Payment System for Secure E-Wallet Apps

Hassan

Shukur

2021

Electronics

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E-wallets are a modern electronic payment system technology that easily recognize consumer interest, making our transactions very convenient and efficient. E-wallets are intended to substitute the existing physical wallet, which may tell others something about us as a person. That is why using a physical wallet is a unique, personal experience that cannot be duplicated. A solution would be to replace the physical wallet with an e-wallet on an existing mobile device. The personal nature of the e-wallet is that it should be installed on a unique device. One of the fundamental protections against any illegal access to e-wallet application is through authentication. In particular, the fundamental authentication category used in an existing e-wallet is based on knowledge (i.e., what you know), ownership (i.e., what you have), and biometric (i.e., what you are) authentication, which are sometimes prone to security threats such as account takeover, sim swapping, app cloning, or know your customer verification attacks. The design of an e-wallet authentication on mobile device solution must take into consideration the intensity of the security. To address this problem, this study proposes a design of e-wallet apps with an extension security element that focuses on the device identity in the existing user authentication mechanism. This study covers four fundamental categories of authentication: password, one time password, fingerprints, and international mobile equipment identifier. Using IMEI limits an e-wallet to be in one specific device in one time; this brings it into line with the nature of a physical wallet. In addition, it will be ready to handle the mentioned threats above, which will ultimately result in the far more reliable to use of e-wallet apps. The proposed authentication design has two phases, a registration phase and an authentication phase. The proposed method has been developed and implemented based on an Android Studio Firebase real-time database management and PayPal. In addition, the complete design has been evaluated using functional requirement testing to see how closely it meets functionality requirements. The results obtained from functional testing show that the functionalities of the proposed method meet the requirements, and one cannot use a same account on two devices; hence, it is secure from attacks. The result also shows that the proposed method has no errors. Moreover, it has been shown that our proposed method has better security parameters in terms of the existing method.

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“…But a wide variety of circumstances and situations impose people to wear masks on faces that are partially occluded or hidden. This common situation includes laboratories, pandemics, immoderate pollution, or medical operations [2]. For exam-ple, as per the WHO and Centers for Disease Control and Prevention (CDC), the better method for protecting people from the COVID-19 virus and being infected or preventing the spreading of the disease is practicing social distancing and wearing face masks.…”

Section: Introductionmentioning

confidence: 99%

Capsule Network-Based Deep Transfer Learning Model for Face Recognition

Sreekala¹,

Cyril

Neelakandan³

et al. 2022

Wireless Communications and Mobile Computing

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Face recognition (FR) is a technique for recognizing individuals through the use of face photographs. The FR technology is widely applicable in a variety of fields, including security, biometrics, authentication, law enforcement, smart cards, and surveillance. Recent advances in deep learning (DL) models, particularly convolutional neural networks (CNNs), have demonstrated promising results in the field of FR. CNN models that have been pretrained can be utilized to extract characteristics for effective FR. In this regard, this research introduces the GWOECN-FR approach, a unique grey wolf optimization with an enhanced capsule network-based deep transfer learning model for real-time face recognition. The proposed GWOECN-FR approach is primarily concerned with reliably and rapidly recognizing faces in input photos. Additionally, the GWOECN-FR approach is preprocessed in two steps, namely, data augmentation and noise reduction by bilateral filtering (BF). Additionally, for feature vector extraction, an expanded capsule network (ECN) model can be used. Additionally, grey wolf optimization (GWO) combined with a stacked autoencoder (SAE) model is used to identify and classify faces in images. The GWO algorithm is used to optimize the SAE model’s weight and bias settings. The GWOECN-FR technique’s performance is validated using a benchmark dataset, and the results are analyzed in a variety of aspects. The GWOECN-FR approach achieved a TST of 0.03 s on the FEI dataset, whereas the AlexNet-SVM, ResNet-SVM, and AlexNet models achieved TSTs of 0.125 s, 0.0051 s, and 0.0062 s, respectively. The experimental results established that the GWOECN-FR technology outperformed more contemporary approaches.

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Masked Face Recognition Using Deep Learning: A Review

Cited by 42 publications

References 157 publications

A survey of uncover misleading and cyberbullying on social media for public health

A survey of uncover misleading and cyberbullying on social media for public health

Device Identity-Based User Authentication on Electronic Payment System for Secure E-Wallet Apps

Capsule Network-Based Deep Transfer Learning Model for Face Recognition

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