Abstract Supervised Descent Method (SDM) is a highly efficient and accurate approach for facial landmark locating and face alignment. In the training phase, it learns a sequence of descent directions to minimize the difference between the estimated shape and the ground truth in feature space. Then in the testing phase, it utilizes these descent directions to predict shape increment iteratively. However, when the facial expression or direction changes too much, the general SDM cannot obtain good performance due to the large variations between the initial shape and the target shape. In this paper, we propose a multi-template SDM (MtSDM) which can maintain high accuracy on training data and meanwhile improve the accuracy on testing data. Instead of only one model is constructed in the training phase, multiple different models are constructed by repeatedly inputting the images which have large variations on expressions or head poses. And in the testing phase, the distances between some specific landmarks are calculated to select an optimal model to update the point location. The experimental results show that our proposed method can improve the performance of traditional SDM and performs better than several existing state-of-the-art methods.

中文翻译：

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人脸对齐的多模板监督下降法

摘要 监督下降法（SDM）是一种高效准确的人脸标志定位和人脸对齐方法。在训练阶段，它学习一系列下降方向，以最小化特征空间中估计形状和地面实况之间的差异。然后在测试阶段，它利用这些下降方向迭代地预测形状增量。然而，当面部表情或方向变化太大时，由于初始形状和目标形状之间的差异较大，一般的SDM无法获得良好的性能。在本文中，我们提出了一种多模板 SDM（MtSDM），它可以保持训练数据的高精度，同时提高测试数据的准确性。而不是在训练阶段只构建一个模型，通过重复输入表情或头部姿势变化较大的图像来构建多个不同的模型。并且在测试阶段，计算一些特定地标之间的距离，以选择最佳模型来更新点位置。实验结果表明，我们提出的方法可以提高传统 SDM 的性能，并且比现有的几种最先进的方法性能更好。