Fast searching of digital face libraries using binary image metrics

We describe a shape comparison method applicable to fast screening of large facial databases. The proposed technique derives holistic similarity measures without the explicit need of point-to-point correspondence thus delivering speed and tolerance to local non-rigid distortions. Specifically, we developed a face similarity measure derived as a variant of the Hausdorff distance by introducing the notion of a neighborhood function and associated penalties. Binary edge representation is used to provide robustness to changes in illumination. Experimental results on a large facial data set demonstrate that our approach produces excellent search results even when less than 1% of the original grey-scale face image information is stored in the face database.