Gesture recognition in dynamic images is challenging computer vision, automation and medical field. Hand gesture tracking between both human must have symmetry real world. With advances sensor technology, numerous researchers recently proposed RGB techniques. In our research paper, we introduce a reliable hand model that accurate despite any complex environment, it can track recognise gestures....

In this paper, we investigate novel data collection and training techniques towards improving classification accuracy of non-moving (static) hand gestures using a convolutional neural network (CNN) frequency-modulated-continuous-wave (FMCW) millimeter-wave (mmWave) radars. Recently, non-contact pose static gesture recognition have received considerable attention in many applications ranging fro...

To control the spreading of corona virus there is a need to advance exchang- ing innovation for supplanting contactless switch. This project deals with design that has no-touch model works entirely on card gestures. smart switch includes sensor can detect movements and translates them into commands controlling lights, fans, various home appliances. The main idea build feasible device switching,...

With the rapid emergence of 3D applications and virtual environments in computer systems; the need for a new type of interaction device arises. This is because the traditional devices such as mouse, keyboard, and joystick become inefficient and cumbersome within these virtual environments. In other words, evolution of user interfaces shapes the change in the Human-Computer Interaction (HCI). In...

this paper presents a comparison study between the multilayer perceptron (mlp) and radial basis function (rbf) neural networks with supervised learning and back propagation algorithm to track hand gestures. both networks have two output classes which are hand and face. skin is detected by a regional based algorithm in the image, and then networks are applied on video sequences frame by frame in...