This paper proposes a complementary regression network (CRN) that combines global and local regression methods to align faces. A global regression network (GRN) generates the coordinates of facial landmark points directly such that all facial feature points are fitted to the input face on the whole and a local regression network (LRN) generates the heatmap of facial landmark points such that each channel localizes the detail of its facial landmark point well. The CRN converts the GRN's coordinates to another heatmap, then uses with the LRN's heatmap to get the final facial landmark points. The CRN works complementarily such that the GRN's overall fitting tendency compensates for the LRN's poor alignment caused by missing local information, whereas the LRN's detailed representation compensates for the GRN's poor alignment caused by global miss-fitting. We conducted several experiments on the 300-W public dataset, the 300-W private dataset, and the Menpo dataset and the proposed CRN achieved 3.14%, 3.74%, and 1.996% the-state-of-art face alignment accuracy in terms of percentage of normalized mean error, respectively.

本文提出了一种互补的回归网络（CRN），该网络结合了全局和局部回归方法来对齐人脸。全局回归网络（GRN）直接生成面部界标点的坐标，从而使所有面部特征点都适合于整个输入面部，而局部回归网络（LRN）生成面部界标点的热图，从而每个通道都可以定位它的面部标志性细节很好。CRN将GRN的坐标转换为另一个热图，然后与LRN的热图一起使用以获取最终的面部界标点。CRN的工作是互补的，因此GRN的整体拟合趋势可以弥补LRN因缺少本地信息而导致的对齐不佳，而LRN的详细表示方式可以弥补GRN' 由于整体失配而导致的对齐不佳。我们在300W公共数据集，300W私有数据集和Menpo数据集上进行了几次实验，提出的CRN在以下方面均达到了最新的人脸对齐精度：3.14％，3.74％和1.996％归一化平均误差的百分比。