face landmark detection(人脸特征检测)
本文参考了如下博文：
（1）http://www.learnopencv.com/facial-landmark-detection/#comment-2471797375
（2）https://blog.csdn.net/bbzz2/article/details/53303043
1. 关键词：
课使用如下关键词搜索论文： “face feature detection”,“facial landmark detection”, “facial keypoint detection”and“face alignment”.
2.算法实现：
（1）Dlib
git clone https://github.com/davisking/dlib.git
Dlib是机器学习，计算机视觉，图像处理，线性代数众多算法的集合。
C++或者Python接口。
Dlib 实现的face landmark detector算法快速，而且准确。
（2）CLM-Framework(C++)
https://github.com/TadasBaltrusaitis/CLM-framework
CLM-Framwork也被称为剑桥人脸跟踪器。基于Dlib。
（3）face++
http://www.faceplusplus.com/demo-landmark/
性能很好，提供了一个易用的API。
但是非开源，需要上传图片到服务器，有隐私方面的担忧。
（4）Real-time facial landmark detection using OpenCV code
https://www.learnopencv.com/facemark-facial-landmark-detection-using-opencv/
In this tutorial, we will learn about facial landmark detection using OpenCV with no external dependencies.
I have written several posts about Facial Landmark Detection and its applications. You can use landmark detection for face morphing, face averaging and face swapping. Until now, we had used the landmark detection that comes with Dlib. It works great, but wouldn’t it be nice if we did not have to depend on any external library.
3.数据
（1）LFPW，
https://neerajkumar.org/databases/lfpw/
Release 1 of LFPW consists of 1,432 faces from images downloaded from the web using simple text queries on sites such as google.com, flickr.com, and yahoo.com. Each image was labeled by three MTurk workers, and 29 fiducial points, shown below, are included in dataset. LFPW was originally described in the following publication:
"Localizing Parts of Faces Using a Consensus of Exemplars,"
Peter N. Belhumeur, David W. Jacobs, David J. Kriegman, Neeraj Kumar,
Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
June 2011.
[pdf] [poster][project page]
（2） Helen
http://www.ifp.illinois.edu/~vuongle2/helen/
In our effort of building a facial feature localization algorithm that can operate reliably and accurately under a broad range of appearance variation, including pose, lighting, expression, occlusion, and individual differences, we realize that it is necessary that the training set include high resolution examples so that, at test time, a high resolution test image can be fit accurately. Although a number face databases exist, we found none that meet our requirements, particularly the resolution requirement. Consequently, we constructed a new dataset using annotated Flickr images.
4.应用
（1）improve face recognition
（2）head pose estimation
（3）face morphing
（4）face replacement
（5） virtual makeover
5，参考论文
1. Active Appearance Model (AAM) by T. Cootes, G. Edwards and C. J. Taylor. [1998]
2. Face Alignment through Subspace Constrained Mean-Shifts by Jason M. Saragih, Simon Lucey and Jeffrey F. Cohn. [2009]
3. Localizing Parts of Faces Using a Consensus of Exemplars by Peter N. Belhumeur, David W. Jacobs, David J. Kriegman, Neeraj Kumar [ 2011 ]
4. Face Alignment by Explicit Shape Regression by Xudong Cao Yichen Wei Fang Wen Jian Sun [2012]
5. Supervised Descent Method and Its Applications to Face Alignment by Xuehan Xiong and Fernando De la Torre [2013]
6. Constrained Local Neural Fields for robust facial landmark detection in the wild by Tadas Baltrusaitis, Peter Robinson, and Louis-Philippe Morency. [2013]
7. Extensive Facial Landmark Localization with Coarse-to-fine Convolutional Network Cascade by Erjin Zhou, Haoqiang Fan, Zhimin Cao, Yuning Jiang and Qi Yin. [2013]
8. Face alignment at 3000 fps via regressing local binary features by S Ren, X Cao, Y Wei, J Sun. [2014]
9. Facial Landmark Detection by Deep Multi-task Learning by Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaoou Tang. [2014]
10.One Millisecond Face Alignment with an Ensemble of Regression Trees by Vahid Kazemi and Josephine Sullivan. [2014]