Precise head segmentation on arbitrary backgrounds

We propose a method for segmentation of frontal human portraits from arbitrary unknown backgrounds. Semantic information is used to project the face into a normalized reference frame. A shape model learned from a set of manually segmented faces is used to compute a rough initial segmentation using a fast iterative algorithm. The rough initial cutout is refined with a boundary based algorithm called 'cluster cutting'. Cluster cutting uses a cost function derived from clustering pixels along the normal of the initial segmentation path with a tree-building algorithm. The result can be refined by the user with an interactive variant of the same algorithm