Learning to simultaneously handle face alignment of arbitrary views, e.g.
frontal and profile views, appears to be more challenging than we thought. The
difficulties lay in i) accommodating the complex appearance-shape relations
exhibited in different views, and ii) encompassing the varying landmark point
sets due to self-occlusion and different landmark protocols. Most existing
studies approach this problem via training multiple viewpoint-specific models,
and conduct head pose estimation for model selection. This solution is
intuitive but the performance is highly susceptible to inaccurate head pose
estimation. In this study, we address this shortcoming through learning an
Ensemble of Model Recommendation Trees (EMRT), which is capable of selecting
optimal model configuration without prior head pose estimation. The unified
framework seamlessly handles different viewpoints and landmark protocols, and
it is trained by optimising directly on landmark locations, thus yielding
superior results on arbitrary-view face alignment. This is the first study that
performs face alignment on the full AFLWdataset with faces of different views
including profile view. State-of-the-art performances are also reported on
MultiPIE and AFW datasets containing both frontaland profile-view faces.

通过学习使用建议树的集成，本文针对人脸对齐中面临的多视角和标记协议等问题提出了解决方案，并在包含前瞻和侧视脸的多个数据集中得到了最新的性能表现。