摘要
In order to distinguish faces of various angles during face recognition, an algorithm of the combination of approximate dynamic programming （ADP） called action dependent heuristic dynamic programming （ADHDP） and particle swarm optimization （PSO） is presented. ADP is used 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, Karhunen-Loeve （K-L） transformation can be used to compress images and decrease data dimensions. According to principal component analysis （PCA）, the main parts of vectors are extracted for data representation. Finally, radial basis function （RBF） neural network is trained 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 accurate efficiency.
In order to distinguish faces of various angles during face recognition, an algorithm of the combination of approximate dynamic programming （ADP） called action dependent heuristic dynamic programming （ADHDP） and particle swarm optimization （PSO） is presented. ADP is used 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, Karhunen-Loeve （K-L） transformation can be used to compress images and decrease data dimensions. According to principal component analysis （PCA）, the main parts of vectors are extracted for data representation. Finally, radial basis function （RBF） neural network is trained 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 accurate efficiency.
基金
This work was supported by Natural Science Foundation of Huazhong University of Science and Technology of PRC(No.2007Q006B).
