Accurate face landmark localization is an essential part of face recognition, reconstruction and morphing. To accurately localize face landmarks, we present our heatmap regression approach. Each model consists of a MobileNetV2 backbone followed by several upscaling layers, with different tricks to optimize both performance and inference cost. We use five na\"ive face landmarks from a publicly available face detector to position and align the face instead of using the bounding box like traditional methods. Moreover, we show by adding random rotation, displacement and scaling -- after alignment -- that the model is more sensitive to the face position than orientation. We also show that it is possible to reduce the upscaling complexity by using a mixture of deconvolution and pixel-shuffle layers without impeding localization performance. We present our state-of-the-art face landmark localization model (ranking second on The 2nd Grand Challenge of 106-Point Facial Landmark Localization validation set). Finally, we test the effect on face recognition using these landmarks, using a publicly available model and benchmarks.

翻译：准确的表面地标定位是面部识别、重建和变形的一个基本部分。为了准确地定位面部地标,我们展示了我们的热映射回归法。每个模型由移动NetV2主干柱组成,然后是几个升级层,同时采用不同的技巧优化性能和推论成本。我们从一个公开的面部探测器中使用5个na\“面部地标标志来定位和调整面部,而不是像传统方法那样使用捆绑框。此外,我们通过添加随机旋转、移动和缩放 -- -- 在对齐后 -- -- 显示模型对面部位置比方向更加敏感。我们还表明,通过使用一个公开的模型和基准,使用一个分解变和像素折叠层的混合,可以降低升缩的复杂度。我们展示了我们最新的面部地标地标定位模型模型(排第二位于106点地标局部地标确认第二大挑战)。最后,我们用这些标志测试了面部识别效果,使用公开的模型和基准。