摘要
在应用AdaBoost算法的人脸检测中,针对训练时间太长及权重调整过适应等问题,提出一种基于特征值等分和双阈值的增强型AdaBoost快速训练算法,给出了双阈值的快速搜索方法。在MIT-CBCL人脸和非人脸训练库上对算法进行了实现。实验结果显示,改进后的双阈值增强型AdaBoost算法简化了训练过程,训练速度提高50倍,收敛速度也更快。使用训练得到的检测器对MIT+CMU人脸测试库进行了测试,结果表明,该方法在检测精度和速度等方面都优于单阈值方法。
Aiming at some problems of too much training time and weight adjustment over fitting in face detection by using AdaBoost. This paper proposes enhanced dual-AdaBoost algorithm based on feature-value-division and dual-threshold, which makes training faster and better. It gives a method to get dual-threshold, and presents the improved mode of weight adjustment. Experimental results on MIT-CBCL training data set illustrate that the dual-AdaBoost makes training process converge quickly, and the training time is as 1/50 as before. Experimental results on MIT＋CMU with the detectors show that the detection speed and precision under the dual-threshold are better than single-threshold method.
出处
《计算机工程》
CAS
CSCD
北大核心
2007年第21期172-174,共3页
Computer Engineering
基金
国家自然科学基金资助项目(60632050
60472060)
江苏省高校自然科学基金资助项目(06KJD520024)
江苏省科技攻关基金资助项目(BE2006357)
淮安市科技发展基金资助项目(HAG05053)
