Spartan Face Mask Detection and Facial Recognition System
According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. Whil...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
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| Series: | Healthcare |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9032/10/1/87 |
| _version_ | 1797493671171457024 |
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| author | Ziwei Song Kristie Nguyen Tien Nguyen Catherine Cho Jerry Gao |
| author_facet | Ziwei Song Kristie Nguyen Tien Nguyen Catherine Cho Jerry Gao |
| author_sort | Ziwei Song |
| collection | DOAJ |
| description | According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, the Spartan Face Detection and Facial Recognition System with stacking ensemble deep learning algorithms is proposed to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms used to classify the features in each scenario. This system is powered by five components including training platform, server, supporting frameworks, hardware, and user interface. Complete unit tests, use cases, and results analytics are used to evaluate and monitor the performance of the system. The system provides cost-efficient face detection and facial recognition with masks solutions for enterprises and schools that can be easily applied on edge-devices. |
| first_indexed | 2024-03-10T01:23:22Z |
| format | Article |
| id | doaj.art-e50d0479a8ce4edb9424fb363cbff996 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-10T01:23:22Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-e50d0479a8ce4edb9424fb363cbff9962023-11-23T13:55:28ZengMDPI AGHealthcare2227-90322022-01-011018710.3390/healthcare10010087Spartan Face Mask Detection and Facial Recognition SystemZiwei Song0Kristie Nguyen1Tien Nguyen2Catherine Cho3Jerry Gao4Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USADepartment of Applied Data Science, San Jose State University, San Jose, CA 95192, USADepartment of Applied Data Science, San Jose State University, San Jose, CA 95192, USADepartment of Applied Data Science, San Jose State University, San Jose, CA 95192, USADepartment of Computer Engineering, San Jose State University, San Jose, CA 95192, USAAccording to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, the Spartan Face Detection and Facial Recognition System with stacking ensemble deep learning algorithms is proposed to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms used to classify the features in each scenario. This system is powered by five components including training platform, server, supporting frameworks, hardware, and user interface. Complete unit tests, use cases, and results analytics are used to evaluate and monitor the performance of the system. The system provides cost-efficient face detection and facial recognition with masks solutions for enterprises and schools that can be easily applied on edge-devices.https://www.mdpi.com/2227-9032/10/1/87COVID-19masked facefacial recognitiondeep learningensemble model |
| spellingShingle | Ziwei Song Kristie Nguyen Tien Nguyen Catherine Cho Jerry Gao Spartan Face Mask Detection and Facial Recognition System Healthcare COVID-19 masked face facial recognition deep learning ensemble model |
| title | Spartan Face Mask Detection and Facial Recognition System |
| title_full | Spartan Face Mask Detection and Facial Recognition System |
| title_fullStr | Spartan Face Mask Detection and Facial Recognition System |
| title_full_unstemmed | Spartan Face Mask Detection and Facial Recognition System |
| title_short | Spartan Face Mask Detection and Facial Recognition System |
| title_sort | spartan face mask detection and facial recognition system |
| topic | COVID-19 masked face facial recognition deep learning ensemble model |
| url | https://www.mdpi.com/2227-9032/10/1/87 |
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