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Adjustment of 3D shape model on facial images using automatic extraction of feature points in model-based image coding

机译:在基于模型的图像编码中使用特征点的自动提取在面部图像上调整3D形状模型

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Abstract: This paper proposes a method for extracting facial feature points automatically using deformable templates whose shape and brightness are changeable. Also, we show adjustment of 3D shape model on facial images using the extracted feature points. The deformable templates are constructed with a mean facial image. The mean facial image is obtained by adjusting 3D shape model on 50 facial images and meaning those. There are the templates for whole face and 6 kinds of facial parts (right eyebrow, left eyebrow, right eye, left eye, nose and mouth). The whole face templates have 7 sizes and 7 levels of brightness for each size. The templates for each facial part have 7 sizes. This algorithm begins from searching face region with the whole face templates. The second step determines candidate region for each facial part by referring the searched face region. The third step searches each facial parts in each candidates region in the order of eyes, nose, eyebrows and mouth. The 3D shape model is adjusted on the facial images using the searched feature points. This method is more stable in extracting feature points than the traditional method with only shape because the shape and brightness of our templates are changeable.!17
机译:摘要:本文提出了一种使用形状和亮度可变的可变形模板自动提取面部特征点的方法。此外,我们使用提取的特征点显示了在面部图像上对3D形状模型的调整。可变形模板由平均面部图像构成。通过在50个面部图像上调整3D形状模型并对其进行含义来获得平均面部图像。有用于整个脸部和6种面部部分(右眉,左眉,右眼,左眼,鼻子和嘴巴)的模板。整个人脸模板具有7种尺寸,每种尺寸都有7种亮度等级。每个面部的模板有7种尺寸。该算法从使用整个人脸模板搜索人脸区域开始。第二步骤通过参考搜索到的脸部区域来确定每个脸部的候选区域。第三步按眼睛,鼻子,眉毛和嘴巴的顺序搜索每个候选区域中的每个面部。使用搜索到的特征点在面部图像上调整3D形状模型。由于模板的形状和亮度是可变的,因此该方法在提取特征点方面比传统的仅使用形状的方法更稳定。!17

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