【摘要】 在人脸检测问题中,需要使用风险敏感的AdaBoost算法来最小化人脸的误分类风险。但是现有的风险敏感的AdaBoost算法对位于分类边界附近的低风险样本的分类性能很差,影响了最终的检测性能。为了解决这个问题,该文通过分析风险敏感的AdaBoost算法的分类错误率,从理论上指出了造成该问题的原因,并据此提出了可控风险敏感的AdaBoost算法。经过实验,该算法在相同召回率的情况下比风险敏感的AdaBoost算法取得了更低的虚警率,并且在CMU正面直立人脸测试集上也获得了更优的人脸检测结果。实验结果表明:该算法在保持风险敏感AdaBoost算法优点的同时,提高了对低风险样本的鉴别能力,获得了更好的性能。更多还原

【Abstract】 The cost-sensitive AdaBoost(CS-AdaBoost) algorithm can be used to minimize the misclassification cost in face detection.However,existing CS-AdaBoost algorithms have difficultly classifying low misclassification cost samples near the classification boundary which influences their performance.This paper theoretically describes how the CS-AdaBoost algorithms’ classification error changes near the boundary.A modified controlled-cost sensitive AdaBoost algorithm(CCS-AdaBoost) is then shown to have lower false alarm rates than CS-AdaBoost with the same recall rate,giving a better detection rate in the CMU frontal test set.The results show that this algorithm maintains the advantages of CS-AdaBoost while improving the ability to classify low misclassification cost samples to give better performance.更多还原