Performance Evaluation of Masked Face Recognition Using Deep Learning for Covid-19 Standard of Procedure (SOP) Compliance Monitoring

Razali, Mohd Norhisham; Shafie, Ana Salwa; Hanapi, Rozita

doi:10.1109/icraie52900.2021.9703986

Cited by 3 publications

(5 citation statements)

References 33 publications

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“…www.ijacsa.thesai.org In particular, four pre-trained CNN model were evaluated for feature extraction, which are Efficient Net (EFFNET) [23], RESNET152 [24], NASNetMobile [25] and MobileNetV2 [26]. EFFNET has been adopted and demonstrated to have an outstanding performance in recent studies such as in the Covid-19 detection based on chest X-Ray [9], [27], smoke detection [8], fake video detection [12], pneumonia classification [10], masked face detection [7] and food recognition [28]. Meanwhile, the RESNET152 was also reported to have a good performance for scene recognition [1].…”

Section: ) Data Acquisitionmentioning

confidence: 99%

“…Deep learning and transfer learning based classification is the emergence approach in any machine learning tasks [4]- [6]. In scene recognition, the pretrained CNN models by using ResNet50 architecture were adopted [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…Although the classification accuracy obtained was good (92.17% and 94.4%), ResNet50 produced larger features dimensionality. Therefore, there are lot of studies in the other domain have various of Efficient Net (EFFNET) CNN architectures such as masked face recognition [7], smoke detection [8], chest X-ray scanning [9]- [11] and fake face video detection [12] due to its exceptional classification performance as well as to generate lightweight features.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the evaluation, the optimal performance of detection is by using EFFNET B4 and B5 and the classification accuracy performance drops when using EFFNET B6 and B7. Next, The EFFNET with Linear SVM were used to address the issues images complexity to recognize the face mask wearing in [7] . In this study, the classification accuracy EFFNET has outperformed the other CNN models using DENSENET201, NASNETLARGE and INCEPTIONRESNETV2 with very light size of features.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis

Razali¹,

Tony²,

Ibrahim³

et al. 2023

IJACSA

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Scene recognition algorithm is crucial for landmark recognition model development. Landmark recognition model is one of the main modules in the intelligent tour guide system architecture for the use of smart tourism industry. However, recognizing the tourist landmarks in the public places are challenging due to the common structure and the complexity of scene objects such as building, monuments and parks. Hence, this study proposes a super lightweight and robust landmark recognition model by using the combination of Convolutional Neural Network (CNN) and Linear Discriminant Analysis (LDA) approaches. The landmark recognition model was evaluated by using several pretrained CNN architectures for feature extraction. Then, several feature selections and machine learning algorithms were also evaluated to produce a super lightweight and robust landmark recognition model. The evaluations were performed on UMS landmark dataset and Scene-15 dataset. The results from the experiments have found that the Efficient Net (EFFNET) with CNN classifier are the best feature extraction and classifier. EFFNET-CNN achieved 100% and 94.26% classification accuracy on UMS-Scene and Scene-15 dataset respectively. Moreover, the feature dimensions created by EFFNet are more compact compared to the other features and even have significantly reduced for more than 90% by using Linear Discriminant Analysis (LDA) without jeopardizing classification performance but yet improved its performance.

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Section: ) Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis

Razali¹,

Tony²,

Ibrahim³

et al. 2023

IJACSA

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show abstract

“…The Deep Convolutional Neural Network (CNN) model and machine learning classifiers for masked face recognition are evaluated in the study by Razali et al ( 67 ). This study specifically examines the feature extractors DENSENET201, NASNETLARGE, INCEPTIONRESNETV2 and EFFICIENTNET (EFFNET).…”

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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Revolutionizing Facial Recognition: A Dolphin Glowworm Hybrid Approach for Masked and Unmasked Scenarios

Naresh Babu KOSURI,

Suneetha MANNE

2024

IJCESEN

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Machine learning has several essential applications, including classification and recognition. Both people and objects may be identified using the Machine learning technique. It is particularly important in the verification process since it recognizes the characteristics of human eyes, fingerprints, and facial patterns. With the advanced technology developments, nowadays, Facial recognition is used as one of the authentication processes by utilizing machine learning and deep learning algorithms and it has been the subject of several academic studies. These algorithms performed well on faces without masks, but not well on faces with masks. since the masks obscured the preponderance of the facial features. As a result, an improved algorithm for facial identification with and without masks is required. After the Covid-19 breakout, deep learning algorithms were utilized in research to recognize faces wearing masks. Those algorithms, however, were trained on both mask- and mask-free faces. Hence, in this, the cropped region for the faces is only used for facial recognition. Here, the features were extracted using the texture features, and the best-optimized features from the glow worm optimization algorithm are used in this paper. With these features set, the hybrid Dolphin glow worm optimization is used for finding the optimal features and spread function value for the neural network. The regression neural network is trained with the optimized feature set and spread function for the face recognition task. The performance of the suggested method will be compared to that of known approaches such as CNN-GSO and CNN for face recognition with and without masks using accuracy, sensitivity, and specificity will next be examined.

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Performance Evaluation of Masked Face Recognition Using Deep Learning for Covid-19 Standard of Procedure (SOP) Compliance Monitoring

Cited by 3 publications

References 33 publications

Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis

Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis

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

Revolutionizing Facial Recognition: A Dolphin Glowworm Hybrid Approach for Masked and Unmasked Scenarios

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