Face detection based on particle swarm optimisation-free entropy minimisation Online publication date: Sun, 17-May-2015
by Xianjun Shen; Yang Yi; Wenyong Dong; Shuaiyu Guo; Junyan Li; Fan Chen
International Journal of Computing Science and Mathematics (IJCSM), Vol. 6, No. 2, 2015
Abstract: For BP neural network has some defects such as slow convergence rate, relatively flat error surface, and easily getting into local minimum. In this paper, it proposes a modified particle swarm optimisation algorithm based on the principle of free entropy minimisation (PSO-FEM), which is used to optimise the BP neural network for face detection. By drawing on the concept of the entropy increase in statistical mechanics, we view the particle swarm as a closed particle system with freedom movement in the solution space, correspond the energy minimisation of the system to the minimum value the particle swarm optimisation algorithm converges to, and correspond the system entropy increase process to the diversified process that the swarm maintains. Simulation results demonstrate that the PSO-FEM algorithm not only obtains ideal recognition results with multiple face image detection in complex background but also has high recognition correctness.
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