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Eyebrow segmentation using active shape models

机译:使用活动形状模型进行眉分割

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Prior research has shown that manually-segmented eyebrows can be used for recognition purposes. However, eyebrow recognition is not as useful without an automated segmentation algorithm. We propose a method to automatically outline the eyebrows in a face using active shape models. We train several models using the images from the Face Recognition Grand Challenge and find that including more landmark points around the eyebrows and including the eyes in the model are beneficial. Our eyebrow active shape model gives a 38.6% improvement over eyebrow segmentation obtained using an open-source face active shape model. When comparing the automatically segmented regions with manual segmentation, we achieve 87% true overlap score with a 12% false overlap score.
机译:先前的研究表明,手动分割的眉毛可用于识别目的。但是,如果没有自动分割算法,眉毛识别就没有用。我们提出一种使用活动形状模型自动勾勒出脸部眉毛轮廓的方法。我们使用“人脸识别大挑战”中的图像训练了多个模型,发现在眉毛周围包括更多界标点以及在模型中包括眼睛是有益的。我们的眉毛活动形状模型比使用开源脸部活动形状模型获得的眉毛分割提高了38.6%。当将自动分割的区域与手动分割的区域进行比较时,我们获得了87%的真实重叠分数和12%的错误重叠分数。

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