3D Human Face Recognition Using Summation Invariants
A novel family of geometrically invariant features, called summation invariants are proposed for the recognition of the 3D surface of human faces. In particular, a 2D semi-local summation invariant feature is extracted from each column and each row of a rectangular region surrounding the nose of a 3D facial depth map. Through extensive experimentation, we empirically identify the most efficient 2D summation invariant features. We also investigate the proper pre-processing method for the 2D summation invariant features. Tested with the 3D facial data from the Face Recognition Grand Challenge v1.0 dataset, the proposed new features exhibit significant performance improvement over the baseline algorithm distributed with the dataset