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Enhance ASM Based on DCT-SVM for Facial Feature Points Localization

机译:基于DCT-SVM的ASM增强面部特征点定位

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Focused on the facial feature points localization,the enhance ASM algorithm based on modeling texture by the DCT-SVM is proposed.First,the statistical shape model is built.Then,some key feature points are selected and their texture models are built by the DCT-SVM.In the subsequent searching,the feature points are divided into two classes based on their reliability gained by DCT-SVM detector,by combining the reliable feature points to the shape constraint,the original shape can finally match to the target face.Experiments show the algorithm is robust to the expressions change and can better locate the features than the traditional ASM.
机译:针对面部特征点定位问题,提出了一种基于DCT-SVM的基于纹理建模的增强ASM算法。首先,建立统计形状模型,然后选择一些关键特征点,并通过DCT建立其纹理模型。 -SVM。在随后的搜索中,根据DCT-SVM检测器获得的可靠性将特征点分为两类,通过将可靠的特征点与形状约束相结合,最终可以将原始形状与目标脸部进行匹配。说明该算法对表达式的更改具有鲁棒性,并且比传统的ASM可以更好地定位特征。

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