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.Comment: Accepted to ACM Multimedia 2023; 10 pages, 7 figures, 6 tables; the
code is at https://github.com/zhiqic/KeyPos