Dynamic Gesture Learning and Recognition Based on the Hermite Neural Network
Qiong Yao · Computer Engineering and Science · 2012
In order to improve the training speed and identification accuracy,a method of dynamic gesture recognition based on the Hermite orthogonal basis feed-forward neural network is put forward.At first,the CamShift algorithm is used to track trajectories of moving fingertips and the characteristic vector of gesture is extracted as the input of the neural network.Then,a feed-forward neural network which hides the layer neurons is activated by a group of Hermite orthogonal polynomial functions is,and a method to determine the network weights directly and determine the number of hidden layer nodes adaptively is proposed.Finally,gestures based on machine vision are recognized through the trained Hermite neural network.The experimental results show that the Hermite neural network can enhance the speed and precision of network training,improve the learning speed and identification accuracy of gesture recognition and has good robustness and generalization ability.