显性形状回归法人脸对齐Face Alignment by Explicit Shape Regression（improving）

We present a very efficient, highly accurate,“Explicit Shape Regression” approach for face alignment.
我们研究出一个非常有效,高度准确,“显式形状回归”的方法，用于实现人脸对齐。

Unlike previous regression-based approaches, we directly learn a vectorial regression function to infer the whole facial shape (a set of facial landmarks) from the image and
explicitly minimize the alignment errors over the training data.
不同于以往的回归方法,我们直接学习矢量回归函数来推断整个面部形状(一组面部标记点)通过图片和明确使对训练数据的对齐错误最小化。

The inherent shape constraint（固有的形状约束） is naturally encoded into the regressor in a cascaded learning framework and applied from coarse to fine during the test, without using a fixed parametric shape model as in most previous methods.
固有的形状约束自然编码到一个级联的回归量学习框架和应用从粗到细的测试期间,不使用一个固定的参数化形状模型在大多数先前的方法。

To make the regression more effective and efficient, we design a two-level boosted regression, shape-indexed features and a correlation-based feature selection method.
为了让回归更有效和准确,我们设计一个两级提高回归,shape-indexed特性和correlation-based特征选择方法。

This combination enables us to learn accurate models from large training data in a short time (20 minutes for 2,000 training images), and run regression extremely fast in test (15 ms for a 87 landmarks shape).
这种组合使我们从大型训练数据在短时间内(20分钟2000训练图像)学习精确模型,并在测试运行回归极快(15ms 87个面部标记点)。

Experiments on challenging data show that our approach significantly outperforms the state-of-the-art in terms of both accuracy and efficiency.
实验针对挑战性数据，测试表明我们的方法明显优于最先进的在准确性和效率方面。

1、Introduction
介绍

Face alignment or locating semantic facial landmarks such as eyes, nose, mouth and chin, is essential for tasks like face recognition, face tracking, face animation and 3D face modeling.
脸对齐或面部语义标记定位例如眼睛,鼻子,嘴,下巴,对人脸识别、人脸跟踪、动画和3d建模等任务至关重要。

With the explosive increase in personal and web photos nowadays, a fully automatic, highly efficient and robust face alignment method is in demand.
爆炸式增长的个人和网站照片现在,一个全自动,高效和健壮的脸对准方法是极为需求。

Such requirements are still challenging for current approaches in unconstrained environments, due to large variations on facial appearance, illumination, and partial occlusions.
这种需求目前仍然具有挑战性的方法在不受约束的环境中,由于面部外观变化较大,照明,部分遮挡。

The alignment error in Eq.(1) is usually used to guide the training and evaluate the performance.
对齐误差通常是用于指导训练和评估性能。

According to how S is estimated, most alignment approaches can be classified into two categories: optimization-based and regression-based.
根据估计,大多数对齐方法可以分为两类:基于优化和基于回归。

2.
Face Alignment by Shape Regression
脸对齐形状回归

In this section, we introduce our basic shape regression framework and how to fit it to the face alignment problem.
在本节中,我们介绍我们的基本形状回归框架以及如何适应它面对对齐问题。

We use boosted regression [9, 8] to combine T weak regressors (R1, …
我们使用了回归(9 8)结合T弱解释变量(R1,……

Rt, …
Rt,……

,RT ) in an additive manner.
RT)以一种添加剂的方式。

Given a facial image I and an initial1 face shape S0, each regressor computes a shape increment S from image features and then updates the face shape, in a cascaded manner:
给定一个面部图像我和一个initial1脸型S0,每个回归量计算形状增量年代从图像特征,然后更新脸型,级联的方式: