Abstract:
Deep Belief Network (DBN) has good performance on facial expressions recognition, but it usually utilizes back propagation to refine the initial parameters of the network in the supervised learning stage. Unfortunately, it is easy to fall into the local optimal only with back propagation approach, and can not find an optimal network parameters for deep belief network. In order to address this problem, a fusion of Particle Swarm Optimization (PSO) and Conjugate Gradient (CG) is proposed to optimize DBN in the supervised learning stage. Firstly, the virtual sample generation method is used to expand training sample data set. Then, the fusion algorithm utilizes PSO to seek the global optimum of the search space. But the high dimension of DBN, using PSO alone to optimize the network parameter space is prone to the local optimization, slow convergence and the other problem.Finally, integrating with CG can enhance the PSO's convergence precision and accelerate its convergence rate,and model parameters may obtain better training. The results show that the proposed method achieves the recognition rates of 94.52% on the JAFFE database and 97.84% on the CK+ database.