IEEE Trans Pattern Anal Mach Intell. 2013 Mar;35(3):624-38. doi: 10.1109/TPAMI.2012.87. Epub 2012 Apr 10.
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.
我们提出了一种自动定位人脸特征点的方法，该方法结合了 3D 形状模型和局部图像补丁分类来处理姿势、表情和身份变化。后者通过密集提取的 SURF 类似特征（我们称之为 DU-SURF）来实现，而前者则基于多类 Hausdorff 距离来解决局部分类错误和不可见点的问题。最终的系统能够在现实场景中定位人脸点，处理不在同一平面的头部旋转、表情变化和不同的光照条件。我们对所提出的方法进行了广泛的实验，结果表明我们的方法优于其他最先进的系统。最后，我们将 DU-SURF 特征与其他现代特征进行了比较，并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。
