  We present a new Cascaded Shape Regression (CSR) architecture, namely Dynamic
Attention-Controlled CSR (DAC-CSR), for robust facial landmark detection on
unconstrained faces. Our DAC-CSR divides facial landmark detection into three
cascaded sub-tasks: face bounding box refinement, general CSR and
attention-controlled CSR. The first two stages refine initial face bounding
boxes and output intermediate facial landmarks. Then, an online dynamic model
selection method is used to choose appropriate domain-specific CSRs for further
landmark refinement. The key innovation of our DAC-CSR is the fault-tolerant
mechanism, using fuzzy set sample weighting for attention-controlled
domain-specific model training. Moreover, we advocate data augmentation with a
simple but effective 2D profile face generator, and context-aware feature
extraction for better facial feature representation. Experimental results
obtained on challenging datasets demonstrate the merits of our DAC-CSR over the
state-of-the-art.
