IEEE Trans Image Process. 2014 Dec;23(12):5108-22. doi: 10.1109/TIP.2014.2361204. Epub 2014 Oct 2.
In the 3D facial animation and synthesis community, input faces are usually required to be labeled by a set of landmarks for parameterization. Because of the variations in pose, expression and resolution, automatic 3D face landmark localization remains a challenge. In this paper, a novel landmark localization approach is presented. The approach is based on local coordinate coding (LCC) and consists of two stages. In the first stage, we perform nose detection, relying on the fact that the nose shape is usually invariant under the variations in the pose, expression, and resolution. Then, we use the iterative closest points algorithm to find a 3D affine transformation that aligns the input face to a reference face. In the second stage, we perform resampling to build correspondences between the input 3D face and the training faces. Then, an LCC-based localization algorithm is proposed to obtain the positions of the landmarks in the input face. Experimental results show that the proposed method is comparable to state of the art methods in terms of its robustness, flexibility, and accuracy.
在三维面部动画和合成领域，输入的面部通常需要通过一组地标进行参数化标记。由于姿势、表情和分辨率的变化，自动三维人脸地标定位仍然是一个挑战。本文提出了一种新颖的地标定位方法。该方法基于局部坐标编码（LCC），由两个阶段组成。在第一阶段，我们执行鼻子检测，这依赖于这样一个事实，即鼻子形状通常在姿势、表情和分辨率的变化下是不变的。然后，我们使用迭代最近点算法找到一个 3D 仿射变换，将输入的面部与参考面部对齐。在第二阶段，我们进行重采样，以建立输入 3D 面部和训练面部之间的对应关系。然后，提出了一种基于 LCC 的定位算法，以获得输入面部地标位置。实验结果表明，该方法在鲁棒性、灵活性和准确性方面与最先进的方法相当。
