Title: Face detection based on particle swarm optimisation-free entropy minimisation
Authors: Xianjun Shen; Yang Yi; Wenyong Dong; Shuaiyu Guo; Junyan Li; Fan Chen
Addresses: School of Computer, Central China Normal University, Wuhan Hubei, China ' School of Computer, Central China Normal University, Wuhan Hubei, China ' School of Computer, Wuhan University, Wuhan Hubei, China ' School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China ' Department of Computer Science, Austin College, Texas, USA ' Department of Computer Science, Hubei Engineering Institute, Huangshi, Hubei, China
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.
Keywords: free entropy minimisation; particle swarm optimisation; PSO; face detection; BP neural networks; facial images; simulation; face recognition; biometrics.
International Journal of Computing Science and Mathematics, 2015 Vol.6 No.2, pp.129 - 138
Received: 14 Jul 2014
Accepted: 22 Aug 2014
Published online: 17 May 2015 *