Cascaded Regression (CR) based methods have been proposed to solve facial
landmarks detection problem, which learn a series of descent directions by
multiple cascaded regressors separately trained in coarse and fine stages. They
outperform the traditional gradient descent based methods in both accuracy and
running speed. However, cascaded regression is not robust enough because each
regressor's training data comes from the output of previous regressor.
Moreover, training multiple regressors requires lots of computing resources,
especially for deep learning based methods. In this paper, we develop a
Self-Iterative Regression (SIR) framework to improve the model efficiency. Only
one self-iterative regressor is trained to learn the descent directions for
samples from coarse stages to fine stages, and parameters are iteratively
updated by the same regressor. Specifically, we proposed Landmarks-Attention
Network (LAN) as our regressor, which concurrently learns features around each
landmark and obtains the holistic location increment. By doing so, not only the
rest of regressors are removed to simplify the training process, but the number
of model parameters is significantly decreased. The experiments demonstrate
that with only 3.72M model parameters, our proposed method achieves the
state-of-the-art performance.

本研究提出了自我迭代回归框架，通过 Landmarks-Attention 网络作为回归器，将级联回归方法中的多个回归器替换为一个自我迭代回归器，从而显着减少了模型参数，提高了模型效率。实验结果表明，该方法仅需 3.72M 模型参数即可达到最先进的性能水平。

基于自我迭代回归的关键点关注网络实现面部特征点检测

Facial Landmarks Detection by Self-Iterative Regression based  Landmarks-Attention Network

Extraction of discriminative features from salient facial patches plays a
vital role in effective facial expression recognition. The accurate detection
of facial landmarks improves the localization of the salient patches on face
images. This paper proposes a novel framework for expression recognition by
using appearance features of selected facial patches. A few prominent facial
patches, depending on the position of facial landmarks, are extracted which are
active during emotion elicitation. These active patches are further processed
to obtain the salient patches which contain discriminative features for
classification of each pair of expressions, thereby selecting different facial
patches as salient for different pair of expression classes. One-against-one
classification method is adopted using these features. In addition, an
automated learning-free facial landmark detection technique has been proposed,
which achieves similar performances as that of other state-of-art landmark
detection methods, yet requires significantly less execution time. The proposed
method is found to perform well consistently in different resolutions, hence,
providing a solution for expression recognition in low resolution images.
Experiments on CK+ and JAFFE facial expression databases show the effectiveness
of the proposed system.

本文提出了一种用于面部表情识别的新框架，通过使用选择面部补丁的外观特征，使用一对一分类方法来分类每对表情类，提取包含区分特征的显著补丁，并针对不同表情类选择不同的显著补丁，提出了一个自动学习 - 无费用的面部标记检测技术，该技术在不同分辨率下表现稳定，在 CK + 和 JAFFE 面部表情数据库上展示了该系统的有效性。