An Effective Hybrid ADP-PSO Strategy for Optimization and Its Application to Face Recognition

Abstract

In order to distinguish faces of various angles during face recognition, an algorithm of the combination of approximate dynamic programming (ADP) which is called action dependent heuristic dynamic programming (ADHDP) and particle swarm optimization (PSO) is presented and used, that is to say, ADP is applied for dynamically changing the values of the PSO parameters. During the process of face recognition, the discrete cosine transformation (DCT) is first introduced to reduce negative effects. Then K-L transformation can be used to compress images and decrease data dimensions. According to principal component analysis (PCA), main parts of vectors are extracted for data representation. Finally, radial basis function (RBF) neural network is enrolled to recognize various faces. The training of RBF neural network is exploited by ADP-PSO. In terms of ORL face database, the experimental result gives a clear view of its highly accurate efficiency.