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Accurate facial landmark detection is critical for facial analysis tasks, yetprevailing heatmap and coordinate regression methods grapple with prohibitivecomputational costs and quantization errors. Through comprehensive theoreticalanalysis and experimentation, we identify and elucidate the limitations ofexisting techniques. To overcome these challenges, we pioneer the applicationof True-Range Multilateration, originally devised for GPS localization, tofacial landmark detection. We propose KeyPoint Positioning System (KeyPosS) -the first framework to deduce exact landmark coordinates by triangulatingdistances between points of interest and anchor points predicted by a fullyconvolutional network. A key advantage of KeyPosS is its plug-and-play nature,enabling flexible integration into diverse decoding pipelines. Extensiveexperiments on four datasets demonstrate state-of-the-art performance, withKeyPosS outperforming existing methods in low-resolution settings despiteminimal computational overhead. By spearheading the integration ofMultilateration with facial analysis, KeyPosS marks a paradigm shift in faciallandmark detection. The code is available at https://github.com/zhiqic/KeyPosS.

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