Emotion recognition in the times of COVID19: Coping with face masks

Magherini, R.; Mussi, Elisa; Servi, Michaela; Volpe, Yary

doi:10.1016/j.iswa.2022.200094

Cited by 10 publications

(5 citation statements)

References 28 publications

(29 reference statements)

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“…For instance, ref. [206] validates a real-time CNN-based emotion recognition approach on a modified version of the AffectNet dataset. Synthetic face masks have been added to each subject from the AffectNet dataset.…”

Section: Difficulty In Recognizing People's Emotionsmentioning

confidence: 81%

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: Difficulty In Recognizing People's Emotionsmentioning

confidence: 81%

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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“…Consequently, a model for automatic emotion recognition includes a number of attention and applications. Applications of computer vision, as well as psychological research, include detecting depression in people as well as diagnosing developmental disorders in children by observing their gaze and facial expressions during social interactions [7][8][9]. Other applications such as monitoring the conditions of the driver (such as the state of fatigue) as well as monitoring signs of attention are used to improve driver safety.…”

Section: Introductionmentioning

confidence: 99%

Spiral Search Ant Colony Optimization Based AlexNet Model for Emotion Recognition

2023

IJIES

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Emotion recognition is useful in understanding human recognition and is necessary for a variety of applications. Existing methods involve applying deep learning (DL) based techniques in emotion recognition and have the drawbacks of overfitting. This research involves applying the spiral search -Ant colony optimization (SS-ACO) for the optimal learning rate of the AlexNet model. The SS-ACO-AlexNet model is applied in the input face image for feature extraction. The extracted features are applied to long short term memory (LSTM) model for emotion classification. The spiral search technique is applied to increase the exploitation of the model and helps to escape the local optima trap. The SS-ACO method selects optimal learning rate and selective dropout to overcome overfitting problems in classification. On YALE datasets, the existing stochastic gradient descent linear collaborative discriminant regression classification (SGD-LCDRC) has 90.07% accuracy, which are lower when compared to proposed SS-ACO method, which has 99.2% accuracy.

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Emotion recognition in the times of COVID19: Coping with face masks

Cited by 10 publications

References 28 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

Spiral Search Ant Colony Optimization Based AlexNet Model for Emotion Recognition

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