Benchmarking of an Enhanced Grasshopper for Feature Map Optimization of 3D and Depth Map Hand Gestures

Abstract

The Enhanced Grasshopper Optimizer (EGO) for the feature map optimization of 3D and depth map hand gestures is the objective of this paper's benchmarking experiment. Using a dataset of 3D and depth map hand gestures, the effectiveness of the EGO algorithm is examined and contrasted to alternative optimizers. The optimized feature map is tested using the Rosenbrock benchmark test function with EGO and SGD, the findings demonstrate that the EGO algorithm performs better than the alternative techniques in terms of precision and computational time. The execution time of EGO is also benchmarked in this study with the different numbers of input features and shows dominance in performing feature selection for 3D hand gesture detection and classification.