基于CPSO－AdaBoost算法的人脸检测方法

Ada Boost分类器训练算法对特征搜索的时间复杂度较高,改进的PSO-Ada Boost算法采用最佳特征搜索方式训练耗时减少,但在迭代过程中容易陷入局部最优解。为此,提出用混沌粒子群优化Ada Boost训练算法的CPSO-Ada Boost算法。通过引入混沌优化序列增加种群的多样性并扩大粒子搜索范围,帮助粒子克服"惰性"摆脱局部最优解,从而在训练分类器时可以快速寻找到性能更好的弱分类器。在MIT样本库上训练人脸检测分类器结果表明,CPSO-Ada Boost算法减少了训练过程中所需要的特征数量,缩短了训练时间,有效地提高了人脸检测率。

Face detection using CPSO-AdaBoost algorithm

The time complexity of AdaBoost classifier training algorithm search feature is high, the PSO-AdaBoost algorithm search for the best features, reduce training time-consuming. However, in the iterative process easy to fall into local optimal solutions. To this end, raise using chaotic particle swarm optimization AdaBoost training algorithm called CPSO-AdaBoost. By introducing the chaos optimization sequence increase the diversity of population and expand the range of particle search range, help particles to overcome the "inertia" and get rid of local optimal solutions, it can quickly find the weak classifiers with better performance when training a classifier. On the MIT sample library, training face detection classification results showed, CPSO-AdaBoost algorithm reducing the number of features needed for the training process, reduce the training time, and effectively improve the human face detection rate.