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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Pose and Expression Independent Facial Landmark Localization Using Dense-SURF and the Hausdorff Distance
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Pose and Expression Independent Facial Landmark Localization Using Dense-SURF and the Hausdorff Distance

机译：使用Dense-SURF和Hausdorff距离的姿势和表情独立面部地标定位

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摘要

We present an approach to automatic localization of facial feature points which deals with pose, expression, and identity variations combining 3D shape models with local image patch classification. The latter is performed by means of densely extracted SURF-like features, which we call DU-SURF, while the former is based on a multiclass version of the Hausdorff distance to address local classification errors and nonvisible points. The final system is able to localize facial points in real-world scenarios, dealing with out of plane head rotations, expression changes, and different lighting conditions. Extensive experimentation with the proposed method has been carried out showing the superiority of our approach with respect to other state-of-the-art systems. Finally, DU-SURF features have been compared with other modern features and we experimentally demonstrate their competitive classification accuracy and computational efficiency.
机译：我们提出了一种自动定位面部特征点的方法，该方法结合了3D形状模型与局部图像补丁分类来处理姿势，表情和身份变化。后者是通过密集提取的类似于SURF的特征（我们称为DU-SURF）来执行的，而前者是基于Hausdorff距离的多类版本来解决局部分类错误和不可见点的。最终的系统能够在实际场景中定位面部点，处理面外旋转，表情变化和不同的光照条件。已经对提出的方法进行了广泛的实验，显示了我们的方法相对于其他最新系统的优越性。最后，将DU-SURF功能与其他现代功能进行了比较，我们通过实验证明了它们具有竞争力的分类准确性和计算效率。

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