Fast multi-view face alignment via multi-task auto-encoders

Face alignment is an important problem in computer vision. It is still an open problem due to the variations of facial attributes (e.g., head pose, facial expression, illumination variation). Many studies have shown that face alignment and facial attribute analysis are often correlated. This paper develops a two-stage multi-task Auto-encoders framework for fast face alignment by incorporating head pose information to handle large view variations. In the first and second stages, multi-task Auto-encoders are used to roughly locate and further refine facial landmark locations with related pose information, respectively. Besides, the shape constraint is naturally encoded into our two-stage face alignment framework to preserve facial structures. A coarse-to-fine strategy is adopted to refine the facial landmark results with the shape constraint. Furthermore, the computational cost of our method is much lower than its deep learning competitors. Experimental results on various challenging datasets show the effectiveness of the proposed method.