基于特征裁剪的双阈值Adaboost人脸检测算法

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Adaboost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。

Dual-threshold Adaboost Face Detection Algorithm Based on Feature Pruning

Aiming at the problem of too much training time by using traditional Adaboost,this paper proposes a novel dual-Adaboost face deteetion algorithm based feature pruning. On the one hand,the usage of dual-threshold weak classifiers which replaced 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.