Heatmap regression (HR) has become one of the mainstream approaches for face alignment and has obtained promising results under constrained environments. However, when a face image suffers from large pose variations, heavy occlusions and complicated illuminations, the performances of HR methods degrade greatly due to the low resolutions of the generated landmark heatmaps and the exclusion of important high-order information that can be used to learn more discriminative features. To address the alignment problem for faces with extremely large poses and heavy occlusions, this paper proposes a heatmap subpixel regression (HSR) method and a multi-order cross geometry-aware (MCG) model, which are seamlessly integrated into a novel multi-order high-precision hourglass network (MHHN). The HSR method is proposed to achieve high-precision landmark detection by a well-designed subpixel detection loss (SDL) and subpixel detection technology (SDT). At the same time, the MCG model is able to use the proposed multi-order cross information to learn more discriminative representations for enhancing facial geometric constraints and context information. To the best of our knowledge, this is the first study to explore heatmap subpixel regression for robust and high-precision face alignment. The experimental results from challenging benchmark datasets demonstrate that our approach outperforms state-of-the-art methods in the literature.

中文翻译：

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通过多阶高精度沙漏网络进行稳健的人脸对齐

热图回归（HR）已成为面部对齐的主流方法之一，并且在受限环境下获得了可喜的结果。但是，当人脸图像的姿势变化大，遮挡力大和照明复杂时，由于生成的地标热图的分辨率低以及排除了可用于学习的重要高阶信息，因此HR方法的性能会大大降低更具歧视性的功能。为了解决姿势非常大且遮挡很重的面部的对齐问题，本文提出了一种热图亚像素回归（HSR）方法和多阶交叉几何感知（MCG）模型，将它们无缝集成到一种新颖的多阶中高精度沙漏网络（MHHN）。提出了一种通过精心设计的子像素检测损耗（SDL）和子像素检测技术（SDT）来实现高精度地标检测的HSR方法。同时，MCG模型能够使用建议的多阶交叉信息来学习更多区分性表示，以增强面部几何约束和上下文信息。据我们所知，这是第一个探索热图亚像素回归以实现稳固和高精度面部对齐的研究。来自具有挑战性的基准数据集的实验结果表明，我们的方法优于文献中的最新方法。MCG模型能够使用建议的多阶交叉信息来学习更多区分性表示，以增强面部几何约束和上下文信息。据我们所知，这是第一个探索热图亚像素回归以实现稳固和高精度面部对齐的研究。来自具有挑战性的基准数据集的实验结果表明，我们的方法优于文献中的最新方法。MCG模型能够使用建议的多阶交叉信息来学习更多区分性表示，以增强面部几何约束和上下文信息。据我们所知，这是第一个探索热图亚像素回归以实现稳固和高精度面部对齐的研究。来自具有挑战性的基准数据集的实验结果表明，我们的方法优于文献中的最新方法。