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. 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，精确的关键点检测对于从人脸识别、表情分析到动画生成等多种应用至关重要。然而，传统的热图或坐标回归方法常面临计算负担大和量化误差等挑战。为解决这些问题，我们提出关键点定位系统（KeyPosS）——一种突破性的面部关键点检测框架，与现有方法截然不同。该框架利用全卷积网络预测距离图，计算兴趣点（POI）与多个锚点之间的距离。通过巧妙利用这些锚点，采用真距多点定位算法对POI位置进行三角测量。值得一提的是，KeyPosS的即插即用特性使其能够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。我们在四个不同数据集上对KeyPosS性能进行了全面评估，与最先进模型进行对比。结果表明，在低分辨率设置下，KeyPosS以极小的额外时间开销大幅领先主流方法。代码开源地址为https://github.com/zhiqic/KeyPosS。