级联方案人脸检测使用非线性分类器

A. Rama, F. Tarrés, A. Soria-Frisch
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引用次数: 0

摘要

本文提出了一种非线性模糊积分算子,用于组合不同的Haar特征集进行人脸检测。该方法在相似的真实接受率和使用相同的最优特征集的情况下,比最先进的AdaBoost人脸检测器具有更低的误检率。此外,这种新的人脸检测器似乎比AdaBoost方法具有更好的泛化能力。实验结果表明,采用四阶段级联方案时,人脸的正检测率大于92%,假检测率为0.1%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cascade scheme face detection using a non-liniar classifier
In this paper, the non-linear fuzzy integral operator is proposed for combining different sets of Haar features for face detection. The proposed method presents a lower false detection rate than the State-of-the-art AdaBoost face detector by a similar true acceptance rate and using the same optimal set of features. Furthermore, this novel face detector seems to have a better generalization capability than the AdaBoost method. Experimental results show a positive face detection rate larger than 92% and a false detection rate of 0.1% when using a four stage cascade scheme.
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