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dc.contributor.author吴暾华
dc.contributor.author周昌乐
dc.date.accessioned2017-11-14T03:19:21Z
dc.date.available2017-11-14T03:19:21Z
dc.date.issued2007-02-01
dc.identifier.citation计算机应用,2007,(02):77-79,86
dc.identifier.issn1001-9081
dc.identifier.otherJSJY200702022
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/156631
dc.description.abstract提出了一种基于AdaBoost算法和C-V方法的人脸特征定位方法。首先根据AdaBoost算法训练样本得到脸、眼、鼻、嘴4个检测器;然后结合人脸边缘图像的先验规则,使用人脸检测器提取人脸区域;接着利用眼、鼻、嘴检测器从人脸区域中检测出人脸特征所在的矩形区域;最后利用C-V方法从各个特征区域中分割出人脸特征的轮廓,进而得到人脸关键特征点的位置。在DTUIMM人脸测试集上,眼睛的检测率为100%,鼻子的检测率为95.3%,嘴巴的检测率为98.4%,提取出的特征点位置准确。实验结果表明方法是有效和鲁棒的。
dc.description.abstractIn this paper, a robust hierarchical approach based on AdaBoost algorithm and C-V method was presented for facial features localization. First, four kinds of detectors were trained by AdaBoost algorithm for detecting faces, eyes, noses and mouths. Second, face regions were detected using the face detector combined with a rule of face edge. Third, the eye regions, nose regions and mouth regions were detected using the facial feature detectors, and the feature contours and feature points were extracted from the feature regions by C-V method. The experiments on DTU_IMM face test set resulted in 94.6% accuracy rate on eyes, 95.3% on noses and 98.4% on mouths, and the positions of the extracted feature points were accurate. Results show that the proposed approach is efficient and robust.
dc.description.sponsorship国家自然科学基金资助项目(60672018);; 厦门大学985二期信息创新平台资助项目(0000-X07204)
dc.language.isozh_CN
dc.subjectAdaBoost算法
dc.subject人脸特征定位
dc.subject角点检测
dc.subject水平集方法
dc.subjectC-V方法
dc.subjectAdaBoost
dc.subjectfacial feature localization
dc.subjectcorner detection
dc.subjectlevel set method
dc.subjectC-V method
dc.title一种鲁棒的人脸特征定位方法
dc.title.alternativeRobust method for facial features localization
dc.typeArticle


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