Face Alignment at 3000 FPS via Regressing Local Binary Features
CVPR2014
https://github.com/yulequan/face-alignment-in-3000fps
https://github.com/luoyetx/face-alignment-at-3000fps
https://github.com/freesouls/face-alignment-at-3000fps
本文针对人脸对齐问题，提出基于 LBP 特征 的二级回归方法，先局部后整体的思路。
基于 shape regression 的人脸对齐 predicts facial shape S in a cascaded manner，每次的迭代量如下式所示：
Φ 是特征提取器， W 表示回归函数
3 Regressing Local Binary Features
这里我们对每个特征点训练一个回归器来提取一个 LBP 特征，
3.1. Learning local binary features Φ
这里使用 regression random forest 学习 each local mapping function
3.2. Learning global linear regression W
学习整体的回归函数 W
3.3. Locality principle
这里应用了 two important regularization methods in feature learning，as guided by a locality principle:
1) we learn a forest for each landmark independently;
2) we only consider the pixel features in the local region of a landmark
下面是解释为什么做出上面的选择
Why the local region?
Intuitively, the optimal radius r should dependon the distributionof ∆s. If ∆s of all trainingsamples are scattered widely, we should use a large r; otherwise we use a small one
As expected, the radius gradually shrinks from early stage to later stage, because the variation of regressed face shapes decreases during the cascade
Why a single landmark regression?
先局部后整体具有一些优势，文中指出了三点：
1） 局部 feature pool 噪声要少点
2）独立的局部更有利于 global learning
3） the local learning is adaptive in different stages，Local learning is actually more appropriate in the late stage
4 Experiments
11