Accurate facial landmark detection is critical for facial analysis tasks, yet prevailing heatmap and coordinate regression methods grapple with prohibitive computational costs and quantization errors. Through comprehensive theoretical analysis and experimentation, we identify and elucidate the limitations of existing techniques. To overcome these challenges, we pioneer the application of True-Range Multilateration, originally devised for GPS localization, to facial landmark detection. We propose KeyPoint Positioning System (KeyPosS) - the first framework to deduce exact landmark coordinates by triangulating distances between points of interest and anchor points predicted by a fully convolutional network. A key advantage of KeyPosS is its plug-and-play nature, enabling flexible integration into diverse decoding pipelines. Extensive experiments on four datasets demonstrate state-of-the-art performance, with KeyPosS outperforming existing methods in low-resolution settings despite minimal computational overhead. By spearheading the integration of Multilateration with facial analysis, KeyPosS marks a paradigm shift in facial landmark detection. The code is available at https://github.com/zhiqic/KeyPosS.

翻译：精准的面部关键点检测是面部分析任务的关键，然而当前的主流热力图与坐标回归方法面临计算成本过高及量化误差等问题。通过全面的理论分析与实验验证，我们识别并阐明了现有技术的局限性。为克服这些挑战，我们首次将源于GPS定位的真距多点定位技术应用于面部关键点检测领域。我们提出关键点定位系统(KeyPosS)——这是首个通过三角测量全卷积网络预测的兴趣点与锚点之间距离来推导精确关键点坐标的框架。KeyPosS的核心优势在于其即插即用特性，可灵活集成至多种解码管线。在四个数据集上的大量实验表明，KeyPosS在极低计算开销的情况下，于低分辨率场景中仍能取得超越现有方法的最优性能。通过率先将多点定位与面部分析相结合，KeyPosS标志着面部关键点检测领域的范式转变。代码已开源至https://github.com/zhiqic/KeyPosS。