Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4948623 | Neurocomputing | 2016 | 19 Pages |
Abstract
Cascaded regression approaches have been widely applied to computer vision tasks recently, and achieve state-of-the-art performance. In this paper, we consider the problem of face alignment model fitting using cascaded regression and propose a new face alignment approach named Joint Local Regressors Learning (JLRL). The main novelty of our learning framework lies in two following aspects: (1) shape constraints among facial landmarks are considered by jointly learning local regressors; (2) the contribution to face alignment errors for each facial landmark is explored in the training phase. Compared with the previous face alignment methods that have shown state-of-the-art performances, our JLRL approach performed best on the LFPW, Helen and 300-W datasets which are the most challenging datasets today.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Yongxin Ge, Cheng Peng, Mingjian Hong, Sheng Huang, Dan Yang Dan Yang,