Abstract:

In order to improve the training speed and identification accuracy of dynamic gesture, a method of gesture recognition based on the particle swarm optimization(PSO) BP neural network is  put forward. First, a set of dynamic gestures is defined for HumanMachine Interaction (HMI). The engenvectors vectors of dynamic gestures are extracted as the input of the BP neural network on the basis of obtaining the trajectories of moving fingertips. An improved PSO algorithm is used to train the BP neural network and get the weights/thresholds of the network. Finally, the gestures based on machine vision are  recognized through the trained BP neural network. The experimental results show that the proposed PSO algorithm can enhance the speed and precision of network training, and improve the accuracy of dynamic gesture recognition.

Key words: machine vision;BP neural network;dynamic gesture recognition;particle swarm optimization