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.

本文提出了一种新的级联形状回归、动态注意力控制级联形状回归 (DAC-CSR) 的架构，用于非约束性人脸的鲁棒性面部关键点检测，包括人脸边界框细化、一般 CSR 和注意力控制 CSR。使用在线动态模型选择方法选择适当的面向域的 CSR 进行关键点的细化，DAC-CSR 的关键创新在于容错机制，使用模糊集样本加权进行注意控制域特定模型的训练。此外，我们提倡使用简单但有效的二维剖面面部生成器和上下文感知特征提取以更好地代表面部特征。实验结果表明，我们的 DAC-CSR 相对于现有技术具有更好的性能。