Face Mask Detection Using Transfer Learning of InceptionV3

Chowdary, G. Jignesh; Punn, Narinder Singh; Sonbhadra, Sanjay Kumar; Agarwal, Sonali

doi:10.1007/978-3-030-66665-1_6

Cited by 154 publications

(41 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Based on accuracy and detection of SOPs, the proposed model is compared with Faster-RCNN [61] ResNet50 [62], VGG16 [53], and InceptionV3 [14]. Table 5 shows that the proposed model has achieved the highest accuracy and can perform mask detection and social distance detection.…”

Section: -Models Comparisonmentioning

confidence: 99%

“…Artificial intelligence techniques are practical and can be employed in different ways to implement these measures to stop the COVID-19 virus from spreading [12]. Emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data, Deep-Learning (DL), and Machine Learning (ML) are utilized to diagnose the COVID-19 cases more quickly [13][14][15][16] for promoting awareness.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Monitoring of COVID-19 SOP in Public Gathering Using Deep Learning Technique

et al. 2021

View full text Add to dashboard Cite

Crowd management has attracted serious attention under the prevailing pandemic conditions of COVID-19, emphasizing that sick persons do not become a source of virus transmission. World Health Organization (WHO) guidelines include maintaining a safe distance and wearing a mask in gatherings as part of standard operating procedures (SOP), considered thus far the most effective preventive measures to protect against COVID-19. Several methods and strategies have been used to construct various face detection and social distance detection models. In this paper, a deep learning model is presented to detect people without masks and those not keeping a safe distance to contain the virus. It also counts individuals who violate the SOP. The proposed model employs the Single Shot Multi-box Detector as a feature extractor, followed by Spatial Pyramid Pooling (SPP) to integrate the extracted features to improve the model's detecting capabilities. The MobilenetV2 architecture as a framework for the classifier makes the model highly light, fast, and computationally efficient, allowing it to be employed in embedded devices to do real-time mask and social distance detection, which is the sole objective of this research. This paper's technique yields an accuracy score of 99% and reduces the loss to 0.04%. Doi: 10.28991/esj-2021-SPER-14 Full Text: PDF

show abstract

Section: -Models Comparisonmentioning

confidence: 99%

mentioning

confidence: 99%

Real-Time Monitoring of COVID-19 SOP in Public Gathering Using Deep Learning Technique

et al. 2021

View full text Add to dashboard Cite

show abstract

“…An improvement of 15.6% was achieved on state-of-the-art at that time. Chowdary et al developed a system that performs face mask detection to identify individuals who were not wearing a mask with a very high accuracy [18]. Image augmentation was performed on the SMFD dataset to increase the size of the training data.…”

Section: Literature Reviewmentioning

confidence: 99%

Is Face Recognition with Masks Possible?

Saib¹,

Pudaruth²

2021

IJACSA

View full text Add to dashboard Cite

With the recent outbreak of the COVID-19 pandemic, wearing face masks has become extremely important to protect us, and to reduce the spread of the virus. This measure has made many existing face recognition systems ineffective as they were trained to work with unmasked faces. In this paper, several methods have been proposed for masked face recognition. Two pre-trained deep learning architectures (VGG16, and MobileNetV2) and the Histogram of Gradients (HOG) technique were used to extract the relevant features from face images of celebrities. A SoftMax layer and Support Vector Machines (SVM) were used for classification. Five scenarios were devised to assess the different models and approaches. With an accuracy of 96.8%, the best model was obtained with MobileNetV2 with a SoftMax layer on the dataset consisting of a mixture of masked and unmasked images. Three different types of masks were also used in this study. The mean accuracy was 91.35% when the same type of mask is used for training and testing. However, the accuracy dropped by an average of 5.6% when a different type of mask is used for training and testing. A contactless attendance system using the best masked face recognition model has also been implemented.

show abstract

“…Iot technologies capable of detecting the absence or incorrect use (i.e., not covering completely mouth and nose) of this medical device are still rare. However, some machine learning models trained to recognize images of people with or without masks have shown how it is possible, with reasonable accuracy, to verify their presence in public and crowded environments (Chowdary et al, 2020) and hypothesize their use in smart cities that have a dense network of surveillance cameras (Rahman et al, 2020).…”

Section: Internet Of Things Solutions To Fight Covid-19 In Living Environmentsmentioning

confidence: 99%

IoT as Non-Pharmaceutical Interventions for the Safety of Living Environments in COVID-19 Pandemic Age

Gamberini

Pluchino

Bacchin

et al. 2021

Front. Comput. Sci.

View full text Add to dashboard Cite

The outbreak of the Sars-Cov-2 pandemic has changed our perception of safety in shared and public living environments including healthcare facilities, shops, schools, and enterprises. The Internet of Things (IoT) represents a suitable solution for managing anti-pandemic smart devices (e.g., UV lights, smart cameras, etc.) and increasing citizens’ safety in public health crises. In this paper, we highlighted how IoT technologies can be exploited as non-pharmaceutical interventions presenting the SAFE PLACE project as an implementation of this concept. The project meant to design and develop an IoT system to ensure the safety and salubrity of shared environments. Advanced algorithms will be exploited to detect and classify humans’ presence, gathering, usage of personal protective equipment, and considering carefully the privacy protection of individuals.

show abstract

Face Mask Detection Using Transfer Learning of InceptionV3

Cited by 154 publications

References 17 publications

Real-Time Monitoring of COVID-19 SOP in Public Gathering Using Deep Learning Technique

Real-Time Monitoring of COVID-19 SOP in Public Gathering Using Deep Learning Technique

Is Face Recognition with Masks Possible?

IoT as Non-Pharmaceutical Interventions for the Safety of Living Environments in COVID-19 Pandemic Age

Contact Info

Product

Resources

About