Masked face recognition with convolutional neural networks and local binary patterns

Vu, Hoai Nam; Nguyen, Huong; Pham, Cuong

doi:10.1007/s10489-021-02728-1

Cited by 96 publications

(35 citation statements)

References 64 publications

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“…To recognize a face algorithm for the face masked to merging and cropping image based methodology and convolutional block attention module (CBAM). The CBAM module used to target the eyelids and the adjacent regions for each sample's correct cropping is investigated [18]. AlexNet is based on a subset of an artificial neural network (ANN) that is similar to the human visual system.…”

Section: Related Workmentioning

confidence: 99%

Masked Face Recognition Using MobileNet V2 with Transfer Learning

Shukla¹,

Tiwari²

2023

Computer Systems Science and Engineering

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Corona virus (COVID-19) is once in a life time calamity that has resulted in thousands of deaths and security concerns. People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission. During the on-going coronavirus outbreak, one of the major priorities for researchers is to discover effective solution. As important parts of the face are obscured, face identification and verification becomes exceedingly difficult. The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model, to identify the problem of face masked identification. In the first stage, we are applying face mask detector to identify the face mask. Then, the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10 (CIFAR10), Modified National Institute of Standards and Technology Database (MNIST), Real World Masked Face Recognition Database (RMFRD), and Stimulated Masked Face Recognition Database (SMFRD). The proposed model is achieving recognition accuracy 99.82% with proposed dataset. This article employs the four pre-programmed models VGG16, VGG19, ResNet50 and ResNet101. To extract the deep features of faces with VGG16 is achieving 99.30% accuracy, VGG19 is achieving 99.54% accuracy, ResNet50 is achieving 78.70% accuracy and ResNet101 is achieving 98.64% accuracy with own dataset. The comparative analysis shows, that our proposed model performs better result in all four previous existing models. The fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks.

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Section: Related Workmentioning

confidence: 99%

Masked Face Recognition Using MobileNet V2 with Transfer Learning

Shukla¹,

Tiwari²

2023

Computer Systems Science and Engineering

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“…Figure 7 that the increase in research related to MFR goes hand-in-hand with the spread and increase of COVID-19. Vu et al (61) extracted features such as eyebrows and eyes from masked input photos, using a mix of CNN and LocalBinary Pattern (LBP). The system outperforms state-ofthe-art models in terms of efficiency.…”

Section: Mfrmentioning

confidence: 99%

“…Vu et al ( 61 ) extracted features such as eyebrows and eyes from masked input photos, using a mix of CNN and LocalBinary Pattern (LBP). The system outperforms state-of-the-art models in terms of efficiency.…”

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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“…However, many image collections have been recently proposed in literature for MFD and MFR. Vu et al [41] proposed combined Local Binary Pattern (LBP) and deep learning features using RetinaFace, and they provided a new dataset called COMASK20. Wang et al [4] provided three types of facemask datasets: Real-world Masked Face Recognition Dataset (RMFRD), Masked Face Detection Dataset (MFDD), and Simulated Masked Face Recognition Dataset (SMFRD).…”

Section: Related Workmentioning

confidence: 99%

Learning Discriminant Spatial Features With Deep Graph-Based Convolutions for Occluded Face Detection

et al. 2022

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The use of face masks has become a widespread non-pharmaceutical practice to mitigate the transmission of COVID-19. However, achieving accurate facial detection while people wear masks or similar face occlusions is a major challenge. This paper introduces a model to detect occluded or masked faces based on fused convolutional graphs. This model includes a deep neural architecture with two spatial-based graphs that rely on a set of key facial features. First, a distance graph is used to identify geographical similarity between the facial nodes that represent certain key face parts. Second, a correlation graph is formulated to compute the correlations between every two nodes that represent two different augmented facial modalities. Transfer learning is then performed using a pretrained deep architecture as a baseline to map the abstract semantic information into multiple feature filters. Then, discriminant graph convolutions are constructed based on the fusion of distance and correlation graphs. This model evaluates two tasks of facial detection, which are the binary detection of masked or unmasked faces, and multi-category detection of masked, unmasked, or occluded face with no mask. The experimental results on two benchmarking real-world datasets show that the proposed deep learning model is highly effective with an accuracy of 98% achieved in binary detection. Even with high variance in image occlusions, our proposed model has great promise in detecting and distinguishing between types of facial occlusion with an accuracy of 86% reported in multi-category detection.

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Masked face recognition with convolutional neural networks and local binary patterns

Cited by 96 publications

References 64 publications

Masked Face Recognition Using MobileNet V2 with Transfer Learning

Masked Face Recognition Using MobileNet V2 with Transfer Learning

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

Learning Discriminant Spatial Features With Deep Graph-Based Convolutions for Occluded Face Detection

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