A Deep Learning Approach to Facial Expression Recognition in the Presence of Masked Occlusion

Detecting facial expressions is a vital aspect of interpersonal communication. Automatic facial emotion recognition (FER) systems for detecting and analyzing human behavior have been a subject of study for the past decade, and have played key roles in healthcare, crime detection, and other use cases. With the worldwide spread of the COVID-19 pandemic, wearing face masks while interacting in public spaces has become recommended behavior to protect against infection. Therefore, improving existing FER systems to tackle mask occlusion is an extremely important task. In this paper, we analyze how well existing CNN models for FER fare with masked occlusion and present deep CNN architectures to solve this task. We also test some methods to reduce model overfitting, such as data augmentation and dataset balancing. The main metric used to compare the models is accuracy, and the dataset used here is FER2013. Images from FER2013 were covered by masks using a certain transformation, resulting in a new dataset, MFER2013. From our evaluation and experimentation, we found that existing models need to be modified before they can achieve good accuracy on masked datasets. By improving the architecture of the base CNN, we were able to achieve a significantly improved accuracy. © 2022 IEEE.

Walk-Through MR World Approach for Students Using Mixed Reality

Knowledge is prodigious, and learning has no boundary. The curiosity of the one to learn, discover and invent decides the future of the world. Educational Institutions’ main objective is to provide qualitative knowledge to the students and supporting tools used for learning play a major role. The conventional education system is arduous to be used in the current situation of the global pandemic of COVID-19. New methods and tools are required to make learning and imparting knowledge more effective. Applications like Google Meet, Zoom, Cisco WebEx are being used in schools and colleges. When it comes to simulations in technical education, for instance, to develop any electrical circuits, robots, buildings, etc., software like MATLAB, 3Ds MAX, GNU Octave is being used. These methods neither are interactive nor provide an immersive experience to the user. To subdue this problem, Mixed Reality (MR) technology can be utilized as a boon by developing an application where students can have interactive classes, submerging themselves, and gaining the required knowledge. Also, the technical students can simulate their experiments onto the real world, providing an idea of how the world may look when new things are adopted and can undergo a walk-through experience in the MR world.

Deep Learning for COVID-19

Ever since the outbreak in Wuhan, China, a variant of Coronavirus named “COVID 19” has taken human lives in millions all around the world. The detection of the infection is quite tedious since it takes 3–14 days for the symptoms to surface in patients. Early detection of the infection and prohibiting it would limit the spread to only to Local Transmission. Deep learning techniques can be used to gain insights on the early detection of infection on the medical image data such as Computed Tomography (CT images), Magnetic resonance Imaging (MRI images), and X-Ray images collected from the infected patients provided by the Medical institution or from the publicly available databases. The same techniques can be applied to do the analysis of infection rates and do predictions for the coming days. A wide range of open-source pre-trained models that are trained for general classification or segmentation is available for the proposed study. Using these models with the concept of transfer learning, obtained resultant models when applied to the medical image datasets would draw much more insights into the COVID-19 detection and prediction process. Innumerable works have been done by researchers all over the world on the publicly available COVID-19 datasets and were successful in deriving good results. Visualizing the results and presenting the summarized data of prediction in a cleaner, unambiguous way to the doctors would also facilitate the early detection and prevention of COVID-19 Infection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.