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
this https URL

该文介绍了一种基于 True-range Multilateration 算法的面部关键点检测框架 (KeyPoint Positioning System, KeyPosS)，该算法不仅能够准确地快速检测面部关键点，而且具有算力小、精度高等优点。