Facial pose estimation has gained a lot of attentions in many practical
applications, such as human-robot interaction, gaze estimation and driver
monitoring. Meanwhile, end-to-end deep learning-based facial pose estimation is
becoming more and more popular. However, facial pose estimation suffers from a
key challenge: the lack of sufficient training data for many poses, especially
for large poses. Inspired by the observation that the faces under close poses
look similar, we reformulate the facial pose estimation as a label distribution
learning problem, considering each face image as an example associated with a
Gaussian label distribution rather than a single label, and construct a
convolutional neural network which is trained with a multi-loss function on
AFLW dataset and 300W-LP dataset to predict the facial poses directly from
color image. Extensive experiments are conducted on several popular benchmarks,
including AFLW2000, BIWI, AFLW and AFW, where our approach shows a significant
advantage over other state-of-the-art methods.

本论文通过在 AFLW 数据集和 300W-LP 数据集上实现的深度卷积神经网络架构，将面部姿态估计问题转化为标签分布学习问题来解决数据不足的问题，显著地优于其他流行方法。

通过标签分布深度学习进行面部姿态估计

Facial Pose Estimation by Deep Learning from Label Distributions

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。