TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Scaling Up Face Masks Classification Using a Deep Neural Network and Classical Method Inspired Hybrid Technique |
| 영문제목(English Title) |
Scaling Up Face Masks Classification Using a Deep Neural Network and Classical Method Inspired Hybrid Technique |
| 저자(Author) |
Akhil Kumar
Arvind Kalia
Kinshuk Verma
Akashdeep Sharma
Manisha Kaushal
Aayushi Kalia
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| 원문수록처(Citation) |
VOL 16 NO. 11 PP. 3658 ~ 3679 (2022. 11) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Classification of persons wearing and not wearing face masks in images has emerged as a new computer vision problem during the COVID-19 pandemic. In order to address this problem and scale up the research in this domain, in this paper a hybrid technique by employing ResNet-101 and multi-layer perceptron (MLP) classifier has been proposed. The proposed technique is tested and validated on a self-created face masks classification dataset and a standard dataset. On self-created dataset, the proposed technique achieved a classification accuracy of 97.3%. To embrace the proposed technique, six other state-of-the-art CNN feature extractors with six other classical machine learning classifiers have been tested and compared with the proposed technique. The proposed technique achieved better classification accuracy and 1-6% higher precision, recall, and F1 score as compared to other tested deep feature extractors and machine learning classifiers. |
| 키워드(Keyword) |
CNNs
Face masks
Machine learning
Multi-layer perceptron
ResNet-101
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| 파일첨부 |
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