In the realm of facial analysis, accurate landmark detection is crucial for various applications, ranging from face recognition and expression analysis to animation. Conventional heatmap or coordinate regression-based techniques, however, often face challenges in terms of computational burden and quantization errors. To address these issues, we present the KeyPoint Positioning System (KeyPosS), a groundbreaking facial landmark detection framework that stands out from existing methods. For the first time, KeyPosS employs the True-range Multilateration algorithm, a technique originally used in GPS systems, to achieve rapid and precise facial landmark detection without relying on computationally intensive regression approaches. The framework utilizes a fully convolutional network to predict a distance map, which computes the distance between a Point of Interest (POI) and multiple anchor points. These anchor points are ingeniously harnessed to triangulate the POI's position through the True-range Multilateration algorithm. Notably, the plug-and-play nature of KeyPosS enables seamless integration into any decoding stage, ensuring a versatile and adaptable solution. We conducted a thorough evaluation of KeyPosS's performance by benchmarking it against state-of-the-art models on four different datasets. The results show that KeyPosS substantially outperforms leading methods in low-resolution settings while requiring a minimal time overhead. The code is available at https://github.com/zhiqic/KeyPosS.

翻译：在面部分析领域中，准确的关键点检测对于从人脸识别、表情分析到动画制作等多种应用至关重要。然而，传统的热力图或坐标回归方法常面临计算负担和量化误差的挑战。为解决这些问题，我们提出KeyPoint Positioning System（KeyPosS），这是一种突破性的面部关键点检测框架，与现有方法截然不同。KeyPosS首次采用真实距离多点定位算法（该技术最初用于GPS系统），在不依赖计算密集型回归方法的情况下，实现快速精确的面部关键点检测。该框架利用全卷积网络预测距离图，计算兴趣点与多个锚点之间的距离。通过巧妙利用这些锚点，结合真实距离多点定位算法对兴趣点位置进行三角定位。值得注意的是，KeyPosS的即插即用特性使其能无缝集成到任何解码阶段，提供灵活且适应性强的解决方案。我们通过四个不同数据集上的基准测试，全面评估了KeyPosS的性能。结果表明，KeyPosS在低分辨率设置下显著优于主流方法，且仅需极小的额外时间开销。代码开源地址：https://github.com/zhiqic/KeyPosS。