A face model is a mathematical representation of the distinct features of a
human face. Traditionally, face models were built using a set of fiducial
points or landmarks, each point ideally located on a facial feature, i.e.,
corner of the eye, tip of the nose, etc. Face alignment is the process of
fitting the landmarks in a face model to the respective ground truth positions
in an input image containing a face. Despite significant research on face
alignment in the past decades, no review analyses various face models used in
the literature. Catering to three types of readers - beginners, practitioners
and researchers in face alignment, we provide a comprehensive analysis of
different face models used for face alignment. We include the interpretation
and training of the face models along with the examples of fitting the face
model to a new face image. We found that 3D-based face models are preferred in
cases of extreme face pose, whereas deep learning-based methods often use
heatmaps. Moreover, we discuss the possible future directions of face models in
the field of face alignment.

面部模型是人脸独特特征的数学表示。本文为入门者、从业者和研究人员提供了对用于面部对齐的不同面部模型的全面分析，包括模型的解释和训练，以及将模型拟合到新面部图像的示例。研究发现，在面部极端姿势的情况下，3D 模型更受青睐，而基于深度学习的方法通常使用热图。此外，还讨论了面部模型在面部对齐领域的可能未来发展方向。

基于人脸模型的人脸对齐方法调查和分类

A survey and classification of face alignment methods based on face  models

Handwritten text and scene text suffer from various shapes and distorted
patterns. Thus training a robust recognition model requires a large amount of
data to cover diversity as much as possible. In contrast to data collection and
annotation, data augmentation is a low cost way. In this paper, we propose a
new method for text image augmentation. Different from traditional augmentation
methods such as rotation, scaling and perspective transformation, our proposed
augmentation method is designed to learn proper and efficient data augmentation
which is more effective and specific for training a robust recognizer. By using
a set of custom fiducial points, the proposed augmentation method is flexible
and controllable. Furthermore, we bridge the gap between the isolated processes
of data augmentation and network optimization by joint learning. An agent
network learns from the output of the recognition network and controls the
fiducial points to generate more proper training samples for the recognition
network. Extensive experiments on various benchmarks, including regular scene
text, irregular scene text and handwritten text, show that the proposed
augmentation and the joint learning methods significantly boost the performance
of the recognition networks. A general toolkit for geometric augmentation is
available.

本文提出了一种新的文本图像增强方法，使用自定义的参考点来生成更适合训练识别网络的样本，并通过联合学习框架优化训练过程，实验证明该方法显著提高了识别网络的性能。

学习数据增强：文本识别联合数据增强和网络优化

Learn to Augment: Joint Data Augmentation and Network Optimization for  Text Recognition

Facial expression synthesis has drawn much attention in the field of computer
graphics and pattern recognition. It has been widely used in face animation and
recognition. However, it is still challenging due to the high-level semantic
presence of large and non-linear face geometry variations. This paper proposes
a Geometry-Guided Generative Adversarial Network (G2-GAN) for photo-realistic
and identity-preserving facial expression synthesis. We employ facial geometry
(fiducial points) as a controllable condition to guide facial texture synthesis
with specific expression. A pair of generative adversarial subnetworks are
jointly trained towards opposite tasks: expression removal and expression
synthesis. The paired networks form a mapping cycle between neutral expression
and arbitrary expressions, which also facilitate other applications such as
face transfer and expression invariant face recognition. Experimental results
show that our method can generate compelling perceptual results on various
facial expression synthesis databases. An expression invariant face recognition
experiment is also performed to further show the advantages of our proposed
method.

本文提出一种几何引导生成对抗网络 (G2-GAN)，通过面部几何条件（标识点）来引导特定表情的面部纹理合成，实现逼真的与身份保护面部表情合成，并取得了良好的实验效果。