University of Houston

Performing 3D face recognition when only partial 3D data are present in the gallery and probe is a very challenging task. The task is even more challenging when the gallery dataset originates from one side of the face while the probe dataset originates from the other. We present a new method for computing the similarity of partial 3D data for the purpose of face recognition. This method is based on and improves upon Semi-Coupled Dictionary Learning by computing a jointly-optimized solution that incorporates the reconstruction cost, the discrimination cost and the semi-coupling cost. The experiments indicate improvements in recognition performance when compared with existing state-of-the-art methods.