Face Alignment

Face Alignment

cvpr2014 One Millisecond Face Alignment with an Ensemble of Regression Trees

文章介绍(ERT)

本文解决了单张图片中的人脸对齐问题，我们给出了如何采用回归树方法直接从一个稀疏子集估计人脸特征点坐标，实现了高精度的人脸对齐效果。我们提出了基于梯度提高学习的回归树方法，优化损失函数和手工数据标点错误的总和。
人脸对齐效果：

论文主页（http://www.nada.kth.se/~vahidk/face_ert.html）

cvpr2015 Face Alignment by Coarse-to-Fine Shape Searching

文章介绍(CFSS)

摘要：
我们提出一种基于增大形状搜索的新颖人脸对齐算法框架。不同于传统的级联回归人脸对齐方法，从一个初始形状通过不断更新调整到最优形状的过程，我们的方法始于一个粗糙的形状搜索空间,包含不同的人脸形状,并采用粗糙的解决方案来约束形状，以在后续更好地搜索。独特的分阶段迭代和自适应搜索。（1）解决了在联级回归方法中，由于粗略的初始条件造成的对齐偏离问题;(二)提高了在各种高难度变化环境中的鲁棒性。框架在不同的数据集上展示了算法的实时性能和先进的结果。

cvpr2015 Face Alignment using Cascade Gaussian Process Regression Trees

文章介绍

英文摘要：In this paper, we propose a face alignment method that uses cascade Gaussian process regression trees (cGPRT) constructed by combining Gaussian process regression trees (GPRT) in a cascade stage-wise manner. Here, GPRT is a Gaussian process with a kernel defined by a set of trees. The kernel measures the similarity between two inputs as the number of trees where the two inputs fall in the same leaves. Without increasing prediction time, the prediction of cGPRT can be performed in the same framework as the cascade regression trees (CRT) but with better generalization. Features for GPRT are designed using shape indexed difference of Gaussian (DoG) filter responses sampled from local retinal patterns to increase stability and to attain robustness against geometric variances. Compared with the previous CRT-based face alignment methods that have shown state-of-the-art performances, cGPRT using shape-indexed DoG features performed best on the HELEN and 300-W datasets which are the most challenging dataset today.

推荐链接

1、thinkface（http://www.thinkface.cn/forum.php?mod=forumdisplay&fid=88）
2、Chen Change Loy - Publications（http://www.eecs.qmul.ac.uk/~ccloy/publication.html）
3、人脸对齐相关论文（http://www.thinkface.cn/thread-4011-1-1.html）
4、SDM（http://www.thinkface.cn/thread-2913-1-1.html）
5、深度学习神经网络在人脸对齐中的应用（http://www.thinkface.cn/thread-4010-1-1.html）