FreeEnricher: Enriching Face Landmarks without Additional Cost

TL;DR

FreeEnricher is a framework that enhances facial landmark density from sparse datasets using a weakly-supervised approach, achieving state-of-the-art accuracy without extra computational cost.

Contribution

It introduces a novel weakly-supervised method to enrich facial landmarks and a plug-and-play module for existing face alignment networks.

Findings

Achieves state-of-the-art accuracy on dense 300W testset

Improves performance on sparse 300W and WFLW datasets

Enriches landmarks without additional computational cost

Abstract

Recent years have witnessed significant growth of face alignment. Though dense facial landmark is highly demanded in various scenarios, e.g., cosmetic medicine and facial beautification, most works only consider sparse face alignment. To address this problem, we present a framework that can enrich landmark density by existing sparse landmark datasets, e.g., 300W with 68 points and WFLW with 98 points. Firstly, we observe that the local patches along each semantic contour are highly similar in appearance. Then, we propose a weakly-supervised idea of learning the refinement ability on original sparse landmarks and adapting this ability to enriched dense landmarks. Meanwhile, several operators are devised and organized together to implement the idea. Finally, the trained model is applied as a plug-and-play module to the existing face alignment networks. To evaluate our method, we manually label the dense landmarks on 300W testset. Our method yields state-of-the-art accuracy not only in newly-constructed dense 300W testset but also in the original sparse 300W and WFLW testsets without additional cost.