Efficient Facial Landmark Detection via Prior Knowledge-Guided Agents

초록

We present a highly efficient, agent-based framework for facial landmark detection that prioritizes model compactness and computational efficiency over maximum accuracy. Unlike conventional approaches that rely on large, fully supervised models, our method assigns each agent to a specific landmark, enabling it to infer its position solely from local observations and prior knowledge without explicit location awareness or inter-agent communication. Prior knowledge is modeled in two embedding spaces - feature and coordinate - using class-conditional Gaussian distributions. Agents determine landmark detection confidence from deviations to these priors and estimate adaptive hopping behavior through a lightweight policy network. A multi-stage detection strategy further reduces redundant computation by detecting sub-landmarks relative to core landmarks. While our method produces slightly higher normalized mean error than state-of-the-art (SoTA) methods, it achieves over 16× and 41× improvements in space and time complexities, respectively.

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