A 3D face matching framework for facial curves
Among the many 3D face matching techniques that have been developed, are variants of 3D facial curve matching, which reduce the amount of face data to one or a few 3D curves. The faces central profile, for instance, proved to work well. However, the selection of the optimal set of 3D curves and the best way to match them has not been researched systematically. We propose a 3D face matching framework that allows profile and contour based face matching. Using this framework we evaluate profile and contour types including those described in the literature, and select subsets of facial curves for effective and efficient face matching. With a set of eight geodesic contours we achieve a mean average precision (MAP) of 0.70 and 92.5% recognition rate (RR) on the 3D face retrieval track of the Shape Retrieval Contest (SHREC08), and a MAP of 0.96 and 97.6% RR on the University of Notre Dame (UND) test set. Face matching with these curves is time-efficient and performs better than other sets of facial curves and depth map comparison.
face recognition feature detection profile and contour matching