Face Landmark

本文综述了多种面部特征检测方法,包括经典的Active Appearance Model到近年来的深度学习技术,如Face Alignment by Explicit Shape Regression等。文章提供了多篇重要学术论文的链接,覆盖从传统计算机视觉方法到最新的人工智能解决方案。

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Face landmark detection algorithms:


http://www.learnopencv.com/facial-landmark-detection/


Facial Feature Detection Research

Many different approaches have been used to solve this problem and it is difficult to summarize them in a blog post. I am simply linking to some important papers ( with major bias toward recent work ) for people who want to investigate more.

  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]
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