Algorithmic Analysis on Deer Hunting-based Grey Wolf for Dynamic Time Wrapping-based Hand Gesture Recognition

Abstract

Nowadays, dynamic hand gesture recognition has become a complicated work in the recognition of pattern and the communities with consideration of computer vision. This paper tempts to frame an algorithmic analysis on proposed “static and dynamic-oriented hand gesture recognition”. Moreover, the recognition of static and dynamic models is improved by Dynamic Time Warping (DTW) model. In the phase called pre-processing, “grey scale conversion and histogram equalization” is used, whereas, in segmentation, “Active Contour model, and canny edge detection” is employed. Moreover, the significant features are extracted depending on the “Histogram of Oriented Gradients (HOG), and Edge Oriented Histogram (EOH)”, and the feature dimension is reduced by Principle Component Analysis (PCA). The optimal feature selection technique is adopted by the novel “Deer Hunting-based Grey Wolf Optimization (DH-GWO)”. Moreover, the “significant frames in the video” are eliminated by the DTW pattern. Finally, the characters and words are exactly recognized by Neural Network (NN), using the DH-GWO training. Here, the analysis is carried out by varying the random number $e$ from 0.5 to 3.0 of the proposed DH-GWO.