System for the automated segmentation of heads from arbitrary background

We propose a system for the fully automated segmentation of frontal human head portraits from arbitrary unknown background. No user interaction is required at all, as the system is initialized using a standard eye detector. Using this semantic information, the head region is projected into a normalized polar reference frame. Regional and boundary models are learned from the image data to setup an energy function for segmentation. A robust non-local boundary detection scheme is proposed, which minimizes the similarity of fore - and background regions. Additionally, a shape model learned from a large set of manually segmented images is employed as prior information to encourage the segmentation of plausible head shapes. Segmentation is performed as an iterative optimization process, using two different graph-based algorithms.