A hybrid Wavelet Neural Network and Switching Particle Swarm Optimization algorithm for face direction recognition
Face direction recognition is an important research issue in humancomputer interaction. In order to improve recognition accuracy, a novel hybrid approach called Switching Particle Swarm OptimizationWavelet Neural Network (SPSOWNN) is presented. In this model, we employ the recently proposed Switching Particle Swarm Optimization (SPSO) algorithm to optimize the parameters of weights, scale factors, translation factors and threshold in Wavelet Neural Network (WNN). The proposed SPSOWNN method has fast convergence speed and higher learning ability than conventional WNNs. Especially, a mode-dependent velocity updating equation with Markovian switching parameters is introduced in SPSO to overcome the contradiction between the local search and the global search, which makes it easy to jump the local minimum. The experiment results of the recognition for face direction show the feasibility and effectiveness of the proposed method. Compared with Particle Swarm OptimizationWavelet Neural Network (PSOWNN), Genetic AlgorithmWavelet Neural Network (GAWNN) and WNN, the proposed method has much better performance over them.
wavelet neural network switching particle swarm optimization face direction recognition non-homogeneous markov chain