Scaling Up Face Masks Classification Using a Deep Neural Network and Classical Method Inspired Hybrid Technique

• Akhil Kumar,
• Arvind Kalia,
• Kinshuk Verma,
• Akashdeep Sharma,
• Manisha Kaushal,
• Aayushi Kalia,

Vol. 16, No. 11, November 30, 2022
10.3837/tiis.2022.11.011, Download Paper (Free):

• CNNs
• Face masks
• Machine Learning
• Multi-layer perceptron
• ResNet-101

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.

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