Exploiting Voronoi diagram properties in face segmentation and feature extraction

Segmentation of human faces from still images is a research field of rapidly increasing interest. Although the field encounters several challenges, this paper seeks to present a novel face segmentation and facial feature extraction algorithm for gray intensity images (each containing a single face object). Face location and extraction must first be performed to obtain the approximate, if not exact, representation of a given face in an image. The proposed approach is based on the Voronoi diagram (VD), a well-known technique in computational geometry, which generates clusters of intensity values using information from the vertices of the external boundary of Delaunay triangulation (DT). In this way, it is possible to produce segmented image regions. A greedy search algorithm looks for a particular face candidate by focusing its action in elliptical-like regions. VD is presently employed in many fields, but researchers primarily focus on its use in skeletonization and for generating Euclidean distances; this work exploits the triangulations (i.e., Delaunay) generated by the VD for use in this field. A distance transformation is applied to segment face features. We used the BioID face database to test our algorithm. We obtained promising results: 95.14% of faces were correctly segmented; 90.2% of eyes were detected and a 98.03% detection rate was obtained for mouth and nose.