Real-Time Hand Gesture Detection with 3-D Key Points Using Deep Learning

Abstract

The importance of touchless systems for easier Human Machine Interaction (HMI) has been brought to light by recent pandemics. A key component of HMI, gesture detection represents a distinct class of useful computer vision applications. Outstanding outcomes in video processing are demonstrated by Convolutional Neural Networks (CNN) for a range of computer vision applications. This study reviews and compares various CNN versions for gesture detection along with their uses. To determine the correctness of the model, a local dataset of hand gestures is built and tested in conjunction with a synthetic gesture dataset. Using multilayered CNN, palm detection in a video frame is accomplished with efficient foreground-background separation. In a parametric research, these Mean Square Error (MSE) values are then contrasted with the known comparable system. The MSE of 12.6% obtained from the combination of synthetic and local datasets is better than that of recent research. In order to achieve 3 Dimensional (3D) estimation of a hand gesture with the aid of palm recognition, the number of key points allocated for the skeletal representation of the hand is crucial. This further enables the application of the technique in numerous upcoming genres.