基于改进混沌遗传算法的人脸特征选择

FEATURE SELECTION OF FACE RECOGNITION BASED ON IMPROVED CHAOS GENETIC ALGORITHM

针对如何选定PCA特征空间维数的问题,提出了一种基于改进混沌遗传算法的特征选择方法。改进的混沌遗传算法采用两种不同规则的混沌映射,维持了种群的多样性,增强了算法的全局搜索能力。利用改进的混沌遗传算法对PCA变换后的特征向量进行选择,可以快速搜索到最有利于分类的特征子空间。仿真实验表明,该方法不但降低了特征空间的维数,而且获得了比采用其它方法更好的识别性能。

Aiming at the problem of how to determine the dimension of the eigenvectors in Principal Component Analysis （ PCA） ,this paper proposes a novel feature selection method based on an improved chaos genetic algorithm（ ICGA） . The algorithm uses two kinds of chaotic mappings in different ways,which maintains the diversity of population and enhances the global searching ability. Then ICGA is used for feature （ eigenvector） selection after the transformation of PCA,which can quickly find out feature subspace that is most beneficial to classification. The experiment results indicate that the proposed method not only reduces the dimensions of face feature space,but also achieves higher recognition performance than other methods.