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

机译:基于DCT-SVM的面部特征点定位增强ASM

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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:专注于面部特征点定位,提出了基于DCT-SVM建模纹理的增强ASM算法。首先,构建了统计形状模型。然后,选择了一些关键特征点,并且由DCT构建它们的纹理模型-SVM.IN随后的搜索,通过DCT-SVM检测器获得的可靠性,通过将可靠的特征点与形状约束组合,原始形状最终与目标面部最终匹配,将特征点分为两个类。显示算法对表达式更改的稳健性,可以更好地定位与传统ASM的特征。

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