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We present a new Cascaded Shape Regression (CSR) architecture, namely DynamicAttention-Controlled CSR (DAC-CSR), for robust facial landmark detection onunconstrained faces. Our DAC-CSR divides facial landmark detection into threecascaded sub-tasks: face bounding box refinement, general CSR andattention-controlled CSR. The first two stages refine initial face boundingboxes and output intermediate facial landmarks. Then, an online dynamic modelselection method is used to choose appropriate domain-specific CSRs for furtherlandmark refinement. The key innovation of our DAC-CSR is the fault-tolerantmechanism, using fuzzy set sample weighting for attention-controlleddomain-specific model training. Moreover, we advocate data augmentation with asimple but effective 2D profile face generator, and context-aware featureextraction for better facial feature representation. Experimental resultsobtained on challenging datasets demonstrate the merits of our DAC-CSR over thestate-of-the-art.

Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting