The locations of the fiducial facial landmark points around facial components
and facial contour capture the rigid and non-rigid facial deformations due to
head movements and facial expressions. They are hence important for various
facial analysis tasks. Many facial landmark detection algorithms have been
developed to automatically detect those key points over the years, and in this
paper, we perform an extensive review of them. We classify the facial landmark
detection algorithms into three major categories: holistic methods, Constrained
Local Model (CLM) methods, and the regression-based methods. They differ in the
ways to utilize the facial appearance and shape information. The holistic
methods explicitly build models to represent the global facial appearance and
shape information. The CLMs explicitly leverage the global shape model but
build the local appearance models. The regression-based methods implicitly
capture facial shape and appearance information. For algorithms within each
category, we discuss their underlying theories as well as their differences. We
also compare their performances on both controlled and in the wild benchmark
datasets, under varying facial expressions, head poses, and occlusion. Based on
the evaluations, we point out their respective strengths and weaknesses. There
is also a separate section to review the latest deep learning-based algorithms.
The survey also includes a listing of the benchmark databases and existing
software. Finally, we identify future research directions, including combining
methods in different categories to leverage their respective strengths to solve
landmark detection "in-the-wild".

综述了面部关键点检测算法的发展历程，将其分为三种不同的分类方法：全局方法、约束局部模型方法和回归方法，并对其进行了分类讨论，比较了它们在受不同因素影响下的性能表现。此外，还列举了面部关键点检测的基准数据库和现有软件，并指出了未来的研究方向。

面部特征点检测：文献综述

Facial Landmark Detection: a Literature Survey

Holistic methods based on dense trajectories are currently the de facto
standard for recognition of human activities in video. Whether holistic
representations will sustain or will be superseded by higher level video
encoding in terms of body pose and motion is the subject of an ongoing debate.
In this paper we aim to clarify the underlying factors responsible for good
performance of holistic and pose-based representations. To that end we build on
our recent dataset leveraging the existing taxonomy of human activities. This
dataset includes 24,920 video snippets covering 410 human activities in total.
Our analysis reveals that holistic and pose-based methods are highly
complementary, and their performance varies significantly depending on the
activity. We find that holistic methods are mostly affected by the number and
speed of trajectories, whereas pose-based methods are mostly influenced by
viewpoint of the person. We observe striking performance differences across
activities: for certain activities results with pose-based features are more
than twice as accurate compared to holistic features, and vice versa. The best
performing approach in our comparison is based on the combination of holistic
and pose-based approaches, which again underlines their complementarity.

本文通过对人类活动识别中的全局方法和基于姿势的方法的性能分析，证明它们在人类活动识别中高度互补；具体表现在全局方法大多受到轨迹数量和速度的影响，而基于姿势的方法主要受到人物视角的影响；同时，结合全局方法和基于姿势的方法，其性能最佳。