Joint Local Regressors Learning for Face Alignment

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.