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Absolute Contrasts in Face Detection with AdaBoost Cascade

机译:AdaBoost级联在人脸检测中的绝对对比

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摘要

Object detection using AdaBoost cascade classifier was introduced by Viola and Jones in December 2001. This paper presents a modification of their method which allows to obtain even 4-fold decrease in false rejection rate, keeping false acceptance rate - as well as the classifier size and training time - at the same level. Such an improvement is achieved by extending original family of weak classifiers, which is searched through in every step of AdaBoost algorithm, with classifiers calculating absolute value of contrast. Test results given in the paper come from a face localization problem, but the idea of absolute contrasts can be applied to detection of other types of objects, as well.
机译:Viola和Jones在2001年12月提出了使用AdaBoost级联分类器进行目标检测的方法。本文提出了对他们的方法的一种修改,该方法可以使错误拒绝率降低4倍,并保持错误接受率-以及分类器的大小和训练时间-相同水平。通过扩展原始的弱分类器系列(可在AdaBoost算法的每个步骤中进行搜索)并使用分类器计算对比度的绝对值来实现这种改进。本文给出的测试结果来自面部定位问题,但是绝对对比度的思想也可以应用于其他类型的对象的检测。

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