关键词: 支持向量机, 混沌粒子群算法, 惯性权重, 人脸识别

Abstract: To make the particles distribute in the problem search space evenly，and to improve the overall searching ability of the algorithm，an improved algorithm for Chaos Particle Swarm Optimization（CPSO） is proposed based on the analysis of the ergodicity of chaos and inertia weight of the PSO.The improved algorithm uses Circle map to produce a uniform distribution of the chaotic variable track points，and dynamic adjustment of inertia weight is implemented to avoid PSO getting into local optimum.Then a face recognition method using this improved algorithm to train Support Vector Machine（SVM） is presented.The experimental results show that the presented SVM method optimized by CPSO can achieve higher recognition performance.

Key words: Support Vector Machine（SVM）, Chaos Particle Swarm Optimization（CPSO）, inertia weight, face recognition