Extended Supervised Descent Method for Robust Face Alignment

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• 作者：Liu Liu (15)
  Jiani Hu (15)
  Shuo Zhang (15)
  Weihong Deng (15)
  
  15. Beijing University of Posts and Telecommunications ; Beijing ; China
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9010
• 期：1
• 页码：71-84
• 全文大小：1,573 KB
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• 作者单位：Computer Vision - ACCV 2014 Workshops
• 丛书名：978-3-319-16633-9
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Supervised Descent Method (SDM) is a highly efficient and accurate approach for facial landmark locating/face alignment. It learns a sequence of descent directions that minimize the difference between the estimated shape and the ground truth in HOG feature space during training, and utilize them in testing to predict shape increment iteratively. In this paper, we propose to modify SDM in three respects: (1) Multi-scale HOG features are applied orderly as a coarse-to-fine feature detector; (2) Global to local constraints of the facial features are considered orderly in regression cascade; (3) Rigid Regularization is applied to obtain more stable prediction results. Extensive experimental results demonstrate that each of the three modifications could improve the accuracy and robustness of the traditional SDM methods. Furthermore, enhanced by the three-fold improvements, the extended SDM compares favorably with other state-of-the-art methods on several challenging face data sets, including LFPW, HELEN and 300 Faces in-the-wild.