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.

中文翻译：

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使用热图和技巧的亚像素面部地标

准确的人脸界标定位是人脸识别，重建和变形的重要组成部分。为了准确定位人脸地标，我们提出了热图回归方法。每个模型都由一个MobileNetV2主干组成，后面是几个升级层，并具有不同的技巧来优化性能和推理成本。我们使用来自公开的人脸检测器的五个原始人脸标志物来定位和对齐人脸，而不是像传统方法那样使用边界框。此外，我们通过在对齐后添加随机旋转，位移和缩放来表明该模型对脸部位置的敏感度要比对方向的敏感度高，我们还表明可以通过使用反卷积层和像素混洗层的混合来降低缩放比例的复杂性，而不会影响定位性能。我们介绍了最先进的人脸地标定位模型（在“ 106点面部地标定位验证集第二次挑战赛”中排名第二）。最后，我们使用公开可用的模型和基准测试使用这些标志性图像对面部识别的影响。