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