A hybrid human recognition framework using machine learning and deep neural networks

Faces are a crucial environmental trigger. They communicate information about several key features, including identity. However, the 2019 coronavirus pandemic (COVID-19) significantly affected how we process faces. To prevent viral spread, many governments ordered citizens to wear masks in public. I...

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Bibliographic Details
Published in:	PloS one Vol. 19; no. 6; p. e0300614
Main Authors:	Sheneamer, Abdullah M, Halawi, Malik H, Al-Qahtani, Meshari H
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 21-06-2024 Public Library of Science (PLoS)
Subjects:	Algorithms Artificial neural networks Biology and Life Sciences Biometric Identification - methods Biometrics Biometry Computer and Information Sciences Coronaviruses COVID-19 COVID-19 - epidemiology COVID-19 - prevention & control Data mining Deep Learning Detectors Epidemics Face Facial recognition technology Gait Gait - physiology Humans Learning algorithms Machine Learning Medicine and Health Sciences Neural networks Neural Networks, Computer Pandemics Recognition Research and Analysis Methods SARS-CoV-2 - isolation & purification Social Sciences
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Summary:	Faces are a crucial environmental trigger. They communicate information about several key features, including identity. However, the 2019 coronavirus pandemic (COVID-19) significantly affected how we process faces. To prevent viral spread, many governments ordered citizens to wear masks in public. In this research, we focus on identifying individuals from images or videos by comparing facial features, identifying a person's biometrics, and reducing the weaknesses of person recognition technology, for example when a person does not look directly at the camera, the lighting is poor, or the person has effectively covered their face. Consequently, we propose a hybrid approach of detecting either a person with or without a mask, a person who covers large parts of their face, and a person based on their gait via deep and machine learning algorithms. The experimental results are excellent compared to the current face and gait detectors. We achieved success of between 97% and 100% in the detection of face and gait based on F1 score, precision, and recall. Compared to the baseline CNN system, our approach achieves extremely high recognition accuracy.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Current address: Computer Science Department, Jazan University, Jazan, KSA Competing Interests: The authors have declared that no competing interests exist.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0300614