An efficient face segmentation algorithm based on binary partition tree

This paper presents an efficient face segmentation algorithm based on binary partition tree. Skin-like regions are first obtained by integrating the results of pixel classification and watershed segmentation. Facial features are extracted by the techniques of valley detection and entropic thresholding, and are used to refine the skin-like regions. In order to segment the facial regions from the skin-like regions, a novel region merging algorithm is proposed by considering the impact of the common border ratio between adjacent regions, and the binary partition tree is used to represent the whole region merging process. Then the facial likeness of each node in the binary partition tree is evaluated using a set of fuzzy membership functions devised for a number of facial primitives of geometrical, elliptical and facial features. Finally, an efficient algorithm of node selecting in the binary partition tree is proposed for the final face segmentation, which can exactly segment the faces without any underlying assumption. The performance of the proposed face segmentation algorithm is demonstrated by experimental results carried out on a variety of images in different scenarios.