基于特征裁剪的双阈值 Adaboost 人脸检测算法

摘要

Aiming at the problem of too much training time by using traditional Adaboost , this paper proposes a novel dual-Ada-boost face deteetion algorithm based feature pruning .On the one hand , the usage of dual-threshold weak classifiers which re-placed the traditional single-threshold weak classifier improves the classification capability on individual weak classifier .On the other hand , the algorithm uses only the samples with small error rate to train the weak classifier .Experimental results show that the training speed is increased by using less features and a small proportion of the features in this dual -Adaboost algorithm .%针对传统Adaboost算法存在训练耗时长的问题，提出一种基于特征裁剪的双阈值Adaboost算法人脸检测算法。一方面，使用双阈值的弱分类器代替传统的单阈值弱分类器，提升单个弱分类器的分类能力；另一方面，特征裁剪的Ada-boost算法在每轮训练中仅仅利用错误率较小的特征进行训练。实验表明基于特征裁剪的双阈值Adaboost人脸检测算法通过使用较少的特征和减少训练时的特征数量的方式，提高了算法的训练速度。