Cascade regression framework has been shown to be effective for facial
landmark detection. It starts from an initial face shape and gradually predicts
the face shape update from the local appearance features to generate the facial
landmark locations in the next iteration until convergence. In this paper, we
improve upon the cascade regression framework and propose the Constrained Joint
Cascade Regression Framework (CJCRF) for simultaneous facial action unit
recognition and facial landmark detection, which are two related face analysis
tasks, but are seldomly exploited together. In particular, we first learn the
relationships among facial action units and face shapes as a constraint. Then,
in the proposed constrained joint cascade regression framework, with the help
from the constraint, we iteratively update the facial landmark locations and
the action unit activation probabilities until convergence. Experimental
results demonstrate that the intertwined relationships of facial action units
and face shapes boost the performances of both facial action unit recognition
and facial landmark detection. The experimental results also demonstrate the
effectiveness of the proposed method comparing to the state-of-the-art works.

本文提出了一种约束联合级联回归框架（Constrained Joint Cascade Regression Framework, CJCRF）来解决面部分析中面部动作单元识别和面部关键点检测两任务，首先通过学习面部动作单元和面部形状之间的关联约束，然后在约束帮助下，我们迭代地更新面部关键点位置和动作单元激活概率以实现两个任务的协同检测。实验结果证明，面部动作单元和面部形状的纠缠关系提高了面部动作单元识别和面部关键点检测的性能，同时也证明了所提出的方法与现有工作相比的有效性。