Combining Heritance AdaBoost and Random Forests for Face Detection

摘要：

AdaBoost has proved to be an effective method to improve the performance of base classifiers both theoretically and empirically. However, previous studies have shown that AdaBoost might suffer from the overfitting problem, especially for noisy data. In addition, it still needs much time to train the classifier using AdaBoost. In this paper, we focus on designing an algorithm named Heritance AdaBoostRF to solve the two problems. We use Heritance AdaBoost to enhance the detection speed instead of AdaBoost, and Random Forests as the weak learners of Heritance AdaBoost to deal with the overfitting problem. Experiments clearly show the superiority of the proposed method over MIT-CBCL face database and a highest detection rate of 98.94％ is obtained, and experimental results based on unbalanced data sets of MIT+CMU face database showed that the overfitting problem has been improved effectively.

关键词： Heritance AdaBoost Random Forests Overfitting Classifier

作者: Jun-Ying Gan Xiao-Hua Cao Jun-Ying Zeng

作者单位: School of Information Engineering, Wuyi University, Jiangmen, Guangdong, P.R.C.529020

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 666-669

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）