Leveraging Discriminative Cues for Masked Face Recognition in Post COVID World

The post COVID world has completely disrupted our lifestyle, where wearing a mask is necessary to protect ourselves and others from contracting the virus. However, face masks have proved to be challenging for facial biometric systems, in the sense that these systems do not work as expected when wearing masks as nearly half of the face is covered, thus reducing discriminative features that the model can leverage. Most of the existing frameworks rely on the entire face as the input, but as the face is covered, these frameworks do not perform up to the mark. Moreover, training another facial recognition system with mask images is challenging as the availability of datasets is limited, both qualitatively and quantitatively. In this paper, we propose a framework that shows better results without significant training. In the proposed work, firstly we extracted the face using SSD, then by obtaining Facial Landmarks for utilizing the cues from other dis-criminative parts for facial recognition. The proposed framework is able to out-perform other frameworks on facial mask images and also found ~4.5% increment in accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

AC-CovidNet: Attention Guided Contrastive CNN for Recognition of Covid-19 in Chest X-Ray Images

Covid-19 global pandemic continues to devastate health care systems across the world. At present, the Covid-19 testing is costly and time-consuming. Chest X-Ray (CXR) testing can be a fast, scalable, and non-invasive method. The existing methods suffer due to the limited CXR samples available from Covid-19. Thus, inspired by the limitations of the open-source work in this field, we propose attention guided contrastive CNN architecture (AC-CovidNet) for Covid-19 detection in CXR images. The proposed method learns the robust and discriminative features with the help of contrastive loss. Moreover, the proposed method gives more importance to the infected regions as guided by the attention mechanism. We compute the sensitivity of the proposed method over the publicly available Covid-19 dataset. It is observed that the proposed AC-CovidNet exhibits very promising performance as compared to the existing methods even with limited training data. It can tackle the bottleneck of CXR Covid-19 datasets being faced by the researchers. The code used in this paper is released publicly at https://github.com/shivram1987/AC-CovidNet/. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Discriminative Deep Neural Network for COVID-19 Detection

Corona Virus Disease 2019 (COVID-19) is spreading rapidly around the world and poses a serious threat to human life and property. The virus has a devastating effect on the human lungs, and early detection is important to prevent transmission with COVID-19. Therefore, for stopping the further spread of COVID-19, it is important to investigate how to accurately identify COVID-19 from chest x-ray images. Although some deep convolutional neural network-based pneumonia identification methods have achieved good results, these methods do not take discriminative properties into account. Specifically, these methods have only learned the indicative features of pneumonia, but not the discriminant features. Therefore, to solve this problem, we put forward a Discriminant CNN model. By introducing an extra discriminative regularization term, the model has enough power to learn both representative and discriminative features. We conduct experiments on a COVID-19 chest X-ray dataset, and the experimental results present that the mentioned model has a significant promotion in precision compared with other models. © 2021 IEEE.