Fast and Robust Method for Dynamic Gesture Recognition Using Hermite Neural Network

Abstract

Due to its shortcomings such as slow convergence rate and low recognition accuracy, the traditional BP neural networks perform poorly in dynamic gesture recognition, especially for online gesture training. In this paper, a novel adaptive Hermite neural networks algorithm for dynamic gesture recognition was proposed. At first, a three-layer feed-forward neural network, of which hidden layer neurons are activated by a group of Hermite orthogonal polynomial functions was constructed. Based on its special structure, a method to determine the network weights directly was introduced, and a novel algorithm to determine the optimal number of hidden nodes adaptively was proposed. Then a rapid method of fingertips tracking was put forward to get the trajectory of dynamic gesture. At last, dynamic gesture was recognized through the trained Hermite neural network. Experiment results show that Hermite neural network can enhance the speed and precision of network training, improve the learning speed and recognition accuracy, and has good robustness and generalization ability

Development of the Handwritten Numeral Recognition Based on Neural Network via Kinet Sensor

“…It better reflects data distribution structure features. In extraction of digit features, principal curves are used to train extraction of data feature vector; besides, based on a detail analysis of digit principal curve structure features, classification features used for digit recognition are extracted [10].For definition principal curve, suppose is a set of a d-dimensional second-order vector points, as shown in Fig. 1.…”