KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired True-Range Multilateration

TL;DR

KeyPosS introduces a GPS-inspired multilateration approach for facial landmark detection, achieving state-of-the-art accuracy with minimal computational cost and high flexibility across various pipelines.

Contribution

This work pioneers the application of True-Range Multilateration to facial landmark detection, providing a plug-and-play framework that improves accuracy especially in low-resolution scenarios.

Findings

Outperforms existing methods on four datasets.

Achieves state-of-the-art accuracy with low computational overhead.

Effective in low-resolution facial images.

Abstract

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.