ABSTRACT
The increasing and global spread of Coronavirus (COVID-19) has made facemasks imperative and valuable. It established new norms to our way of life with regulations that are necessary for survival. This study portrays the methodological significance of image processing using Deep Learning: MobileNet-v2 cascade for detection of the masked face and spawning face embedding. It achieves the best results for larger datasets as MobileNet-V2 is a convolutional semantic network with a depth of about 53 layers, meanwhile, the application of similar methods on smaller datasets proves challenging. This paper paves a path of exploring detection on the basis of the Single Shot Detector (SSD) algorithm that introduces a channel attention mechanism to improve the ability of the model to express salient features while simultaneously utilizing information of different feature levels optimizing the function loss. It also sheds light on the resultant output, which creates a large chunk of data categorized as big data. The algorithm shows final experimental results predicting the goal of face recognition and mask detention as successful and effective with an accuracy of the results ranging between 90-95%.
ABSTRACT
This paper proposes a real-time hand gesture recognition interface called VUCIS (Virtual User Computer Interaction System) to operate a mouse, keyboard and stylus. OpenCV and MediaPipe Hands are used to achieve this. OpenCV is utilized for its powerful image processing capabilities and computer vision. MediaPipe Hands library is used to generate 21 landmarks on a hand. By using a combination of various landmarks, a distinct gesture is assigned to a task performed by the input devices. The proposed interface allows users to interact with computers easily, reduces the need for keyboard and mouse as external hardware in certain circumstances and eliminates any form of contact with a surface, minimizing the spread of COVID-19.