Automatic Segmentation of Head-and-Shoulder Images by Combining Edge Feature and Shape Prior

Automatic segmentation without any user interaction is very difficult due to potentially high complexity of the scene. No wonder, most existing segmentation algorithms are based on user interactions. However, automatic segmentation in some special situations has great significance. In this paper, we introduce an automatic segmentation algorithm for frontal head-and-shoulder images. Our algorithm combines edge feature and shape prior to extract the foreground silhouette automatically. The novelty of our approach lies in two aspects, namely, the Cost Path Segmentation (CPS) algorithm to extract the initial foreground silhouette, and a general active prior shape model, to extract the final foreground segmentation. We demonstrate the high quality and performance of the proposed approach with a variety of head-and-shoulder images. Compared with previous methods, our approach is much more robust for images with complex color distributions in foreground and background.