Face recognition under mask-wearing based on residual inception networks

Moungsouy, Warot; Tawanbunjerd, Thanawat; Liamsomboon, Nutcha; Kusakunniran, Worapan

doi:10.1108/aci-09-2021-0256

Cited by 7 publications

(3 citation statements)

References 39 publications

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“…In [188], the pre-trained Faster R-CNN Inception ResNet v2 model is used to implement a DTL-based FMD, where the pre-trained model weights on the COCO datasets are used as the starting point for DTL on another FMD dataset, which includes 3300 images with different types of face masks. Besides, because the lower parts of human faces are usually occluded and cannot be utilized in the face recognition learning process, the authors in [189] propose a framework that recognizes human faces using any available facial components. Such components may vary depending on wearing or not wearing masks and on the mask position.…”

Section: Fmd Based On Deep Transfer Learning (Dtl)mentioning

confidence: 99%

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

Himeur

Al-Maadeed

Varlamis

et al. 2023

Systems

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After different consecutive waves, the pandemic phase of Coronavirus disease 2019 does not look to be ending soon for most countries across the world. To slow the spread of the COVID-19 virus, several measures have been adopted since the start of the outbreak, including wearing face masks and maintaining social distancing. Ensuring safety in public areas of smart cities requires modern technologies, such as deep learning and deep transfer learning, and computer vision for automatic face mask detection and accurate control of whether people wear masks correctly. This paper reviews the progress in face mask detection research, emphasizing deep learning and deep transfer learning techniques. Existing face mask detection datasets are first described and discussed before presenting recent advances to all the related processing stages using a well-defined taxonomy, the nature of object detectors and Convolutional Neural Network architectures employed and their complexity, and the different deep learning techniques that have been applied so far. Moving on, benchmarking results are summarized, and discussions regarding the limitations of datasets and methodologies are provided. Last but not least, future research directions are discussed in detail.

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Section: Fmd Based On Deep Transfer Learning (Dtl)mentioning

confidence: 99%

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

Himeur

Al-Maadeed

Varlamis

et al. 2023

Systems

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“…Many other studies were focused on the processing of face images with masks. For example, the analyzed problems addressed face recognition (e.g., [22], [23]) or emotion recognition (e.g., [24], [25]) using face images covered by masks.…”

Section: Introductionmentioning

confidence: 99%

Mask Detection and Classification in Thermal Face Images

Kowalczyk¹,

Sobotka²,

Rumiński³

2023

IEEE Access

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Face masks are recommended to reduce the transmission of many viruses, especially SARS-CoV-2. Therefore, the automatic detection of whether there is a mask on the face, what type of mask is worn, and how it is worn is an important research topic. In this work, the use of thermal imaging was considered to analyze the possibility of detecting (localizing) a mask on the face, as well as to check whether it is possible to classify the type of mask on the face. The previously proposed dataset of thermal images was extended and annotated with the description of a type of mask and a location of a mask within a face. Different deep learning models were adapted. The best model for face mask detection turned out to be the Yolov5 model in the ''nano'' version, reaching mAP higher than 97% and precision of about 95%. High accuracy was also obtained for mask type classification. The best results were obtained for the convolutional neural network model built on an autoencoder initially trained in the thermal image reconstruction problem. The pretrained encoder was used to train a classifier which achieved an accuracy of 91%.INDEX TERMS Deep neural networks, epidemic prevention, health infrastructure, mask area detection, mask type classification, thermal imaging.

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“…The area around the eyes was highlighted in the attention-based portion using the Convolutional Block Attention Module (CBAM) with an accuracy of 92.61%. Based on the FaceNet model and the residual inception network of the Inception-ResNet-v1 architecture, Moungsouy et al [15] developed a method for recognizing human faces in both mask-and non-mask-wearing situations. The simulated masks on the original face images are augmented for the training model.…”

Section: -Introductionmentioning

confidence: 99%

An Optimized Machine Learning and Deep Learning Framework for Facial and Masked Facial Recognition

Thanathamathee¹,

Sawangarreerak²,

Kongkla³

et al. 2023

Emerg Sci J

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In this study, we aimed to find an optimized approach to improving facial and masked facial recognition using machine learning and deep learning techniques. Prior studies only used a single machine learning model for classification and did not report optimal parameter values. In contrast, we utilized a grid search with hyperparameter tuning and nested cross-validation to achieve better results during the verification phase. We performed experiments on a large dataset of facial images with and without masks. Our findings showed that the SVM model with hyperparameter tuning had the highest accuracy compared to other models, achieving a recognition accuracy of 0.99912. The precision values for recognition without masks and with masks were 0.99925 and 0.98417, respectively. We tested our approach in real-life scenarios and found that it accurately identified masked individuals through facial recognition. Furthermore, our study stands out from others as it incorporates hyperparameter tuning and nested cross-validation during the verification phase to enhance the model's performance, generalization, and robustness while optimizing data utilization. Our optimized approach has potential implications for improving security systems in various domains, including public safety and healthcare.

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Face recognition under mask-wearing based on residual inception networks

Cited by 7 publications

References 39 publications

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

Mask Detection and Classification in Thermal Face Images

An Optimized Machine Learning and Deep Learning Framework for Facial and Masked Facial Recognition

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