A novel DCNN-ELM hybrid framework for face mask detection

• Published in: Intelligent systems with applications Vol. 17; p. 200175
• Main Authors: Agarwal, Charu, Itondia, Pranjul, Mishra, Anurag
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-02-2023; Published by Elsevier Ltd; Elsevier
• Subjects: Convolution neural network; COVID-19; Face mask detector; Masked face; Transfer learning; COVID-19; Face mask detector; Masked face; Transfer learning; Convolution neural network
• ISSN: 2667-3053; 2667-3053
• DOI: 10.1016/j.iswa.2022.200175

•Automated face mask detector framework for protection from COVID-19.•Six different pre-trained deep convolution neural networks (DCNN) models are examined.•Five different machine learning classifiers are examined in conjunction with DCNN models.•Experimental outcomes show high degree of accuracy.•Time span computation, feature visualization and ablation study are also performed.
The Coronavirus disease (2019) has caused massive destruction of human lives and capital around the world. The latest variant Omicron is proved to be the most infectious of all its previous counterparts – Alpha, Beta and Delta. Various measures are identified, tested and implemented to minimize the attack on humans. Face masks are one of those measures that are shown to be very effective in containing the infection. However, it requires continuous monitoring for law enforcement. In the present manuscript, a detailed research investigation using different ablation studies is carried out to develop the framework for face mask recognition using pre-trained deep convolution neural networks (DCNN) models used in conjunction with a fast single layer feed-forward neural network (SLFNN) commonly known as Extreme Learning Machine (ELM) as classification technique. The ELM is well known for its real time data processing capabilities and has been successfully applied both for regression and classification problems of image processing and biomedical domain. It is for the first time that in this paper we have proposed the use of ELM as classifier for face mask detection. As a precursor to this, for feature selection, six pre-trained DCNNs such as Xception, Vgg16, Vgg19, ResNet50, ResNet 101 and ResNet152 are tested for this purpose. The best testing accuracy is obtained in case of ResNet152 transfer learning model used with ELM as the classifier. The performance evaluation through different ablation studies on testing accuracy explicitly proves that ResNet152 - ELM hybrid architecture is not only the best among the selected transfer learning models but also proves so when it is compared with several other classifiers used for the face mask detection operation. Through this investigation, novelty of the use of ResNet152 + ELM for face mask detection framework in real time domain is established.