Unconstrained Face Alignment via Cascaded Compositional Learning

We present a practical approach to address the problem of unconstrained face alignment for a single image. In our unconstrained problem, we need to deal with large shape and appearance variations under extreme head poses and rich shape deformation. To equip cascaded regressors with the capability to handle global shape variation and irregular appearance-shape relation in the unconstrained scenario, we partition the optimisation space into multiple domains of homogeneous descent, and predict a shape as a composition of estimations from multiple domain-specific regressors. With a specially formulated learning objective and a novel tree splitting function, our approach is capable of estimating a robust and meaningful composition. In addition to achieving state-of-the-art accuracy over existing approaches, our framework is also an efficient solution (350 FPS), thanks to the on-the-fly domain exclusion mechanism and the capability of leveraging the fast pixel feature.

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Datasets


Introduced in the Paper:

AFLW-19

Used in the Paper:

Multi-PIE AFW AFLW

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Face Alignment AFLW-19 CCL NME_diag (%, Full) 2.72 # 16
NME_diag (%, Frontal) 2.17 # 11

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