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。

关键词: dual-AdaBoost, 双阈值, 人脸检测

Abstract: 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.

Key words: dual-AdaBoost, dual-threshold, face detection

中图分类号: 

• TP391.4