A new approach using modified Hausdorff distances with eigenface for human face recognition
Kwan-Ho Lin, Kin‐Man Lam, Wan-Chi Siu · 2004
Hausdorff distance is an efficient measure of the similarity of two point sets. In this paper, we propose two new spatially weighted Hausdorff distance measures for human face recognition, namely, spatially eigen-weighted Hausdorff distance (SEWHD) and spatially eigen-weighted 'doubly' Hausdorff distance (SEW2HD). These new Hausdorff distances incorporate the information about the location of important facial features so that distances at those regions will be emphasized. The weighting function used in the Hausdorff distance measure is based on an eigenface, which has a large value at locations of important facial features and can reflect the face structure more effectively. Experimental results based on a combination of the ORL, MIT, and Yale face databases show that SEW2HD can achieve recognition rates of 83%, 90% and 92% for the first one, the first three and the first five likely matched faces, respectively, while the corresponding recognition rates of SEWHD are 80%, 83% and 88%, respectively.