ABSTRACT
According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, the Spartan Face Detection and Facial Recognition System with stacking ensemble deep learning algorithms is proposed to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms used to classify the features in each scenario. This system is powered by five components including training platform, server, supporting frameworks, hardware, and user interface. Complete unit tests, use cases, and results analytics are used to evaluate and monitor the performance of the system. The system provides cost-efficient face detection and facial recognition with masks solutions for enterprises and schools that can be easily applied on edge-devices.
ABSTRACT
The virtual practice has made major advances in the way that we care for patients in the modern era. The culture of virtual practice, consulting, and telemedicine, which had started several years ago, took an accelerated leap as humankind was challenged by the novel coronavirus pandemic (COVID19). The social distancing measures and lockdowns imposed in many countries left medical care providers with limited options in evaluating ambulatory patients, pushing the rapid transition to assessments via virtual platforms. In this novel arena of medical practice, which may form new norms beyond the current pandemic crisis, we found it critical to define guidelines on the recommended practice in neurotology, including remote methods in examining the vestibular and eye movement function. The proposed remote examination methods aim to reliably diagnose acute and subacute diseases of the inner-ear, brainstem, and the cerebellum. A key aim was to triage patients into those requiring urgent emergency room assessment versus non-urgent but expedited outpatient management. Physicians who had expertise in managing patients with vestibular disorders were invited to participate in the taskforce. The focus was on two topics: (1) an adequate eye movement and vestibular examination strategy using virtual platforms and (2) a decision pathway providing guidance about which patient should seek urgent medical care and which patient should have non-urgent but expedited outpatient management.