Pose-indexed based multi-view method for face alignment

This paper presents a novel pose-indexed based multi-view (PIMV) face alignment framework. Most of the current cascaded regression face alignment methods generally start with a mean shape. However, when the initial shape is far from the ground truth, the performance significantly deteriorates. Our approach aims to obtain a preferable initial shape from a pose-indexed shape searching space. This space is established by a series of pose-shape pairs which are generally treated as mappings from poses to face shapes. Each shape in this space corresponds to one view which is used as an index of the shape. Subsequently, the index shape is employed as the initial shape for the following iterative stages. The powerful shape-initialization method effectively prevents the local optima problem caused by poor initialization in prediction. Experiments demonstrate that our approach outperforms previous methods on challenging datasets with large pose variations, occlusions and illuminations.