ABSTRACT
The detection of masked face is becoming an essential part of health care safetydue to the pandemic caused by the coronavirus and the surveillance systems. One of the most challenging problems in face recognition systems is the accurate identification of faces in the presence of occlusion like wearing of glasses and masks. The current study proposes a novel convolutional neural network(CNN)-based model for accurate detection of faces in the presence of mask and glasses.The novel architecture of the model was developed using ten convolutional layers, five max-pooling layers, and a dropout layer. The Adam optimizer was used for optimization of the performance our model. Early stopping criteria in conjunction with the ReduceLROnPlateau class was employed to avoid the overfitting problem. Our proposed model could achieve the accuracy of 99.71% on the test dataset suggesting its superiorityto its existing counterparts. Based on the results, the suitability of the proposed model for face detection in the presence of occlution in real-life application has been recommended. Copyright © 2022, Anka Publishers. All rights reserved.
ABSTRACT
Background: In the context of newly emerging diseases, global health security has gained importance in recent years. The pandemic COVID-19 has reemphasized the significance. Despite increased attention to enhance health security, the existing capacities were not optimally prepared to respond to public health threats posed by emerging infectious diseases. Objective: The objective of the study was to develop a simple tool that can help monitor and build up the critical capacity to prevent, detect, respond and help identify the gaps in the preparedness of the health system. Methods: The cross-sectional study was conducted from January 2018 – December 2018 amongst the district level health functionaries like the disease surveillance, laboratory, and the animal health focal points in the selected districts across the three South Indian states. Results: The responses received from the participants of the study were scored and categorized by domains into a biosecurity index, which was easily adaptable to all districts and easy to implement by a district official or state health officer for the initial assessment. Data acquired from this tool could be analysed to build and inform readiness and response plans for preventing and controlling health emergencies. Conclusion: The biosecurity index developed for the Indian districts was an appropriate instrument for epidemic preparedness assessment. © 2021 Vyas et al.