Application of AdaBoost on Face Detection

摘要：

In this paper, we implement a face detection model using AdaBoost 1. By boosting different weak learners, we learn that the boosting process can change the ranks of the basic learners. The experiments also show that the error rate of AdaBoost keeps decreasing when the training set size increases, while other approaches like SVM and logistical regression perform worse with a larger training set due to overfitting.

关键词： Adaboost Face Detection Machine Learning

作者: Liangjun LI Hongliang ZHANG Ming YANG Tienan LI

作者单位: Computing Center of Anshan Normal University Anshan,Liaoning China 114005 School of Computer and Automatic Control Hebei United University

会议类型: 国际会议

会议名称: The 13th IEEE Joint International Computer Science and Information Technology Conference(2011年第13届IEEE联合国际计算机科学与信息技术会议 JICSIT 2011)

会议地点: 重庆

会议语种:英文

页码: 370-373

在线出版日期: 2011-08-20（万方平台首次上网日期，不代表论文的发表时间）