3D face recognition in the presence of partial data: A semi-coupled dictionary learning approach

Performing face recognition under extreme poses and lighting conditions remains a challenging task for current state-of-the-art biometric algorithms. The recognition task is even more challenging when there is insufficient training data available in the gallery, or when the gallery dataset originates from one side of the face while the probe dataset originates from the other. The authors present a new method for computing the distance between two biometric signatures acquired under such challenging conditions. This method improves upon an existing Semi-Coupled Dictionary Learning method by computing a jointly-optimized solution that incorporates the reconstruction cost, the discrimination cost, and the semi-coupling cost. The use of a semi-coupling term allows the method to handle partial 3D face meshes where, for example, only the left side of the face is available for gallery and the right side of the face is available for probe. The method also extends to 2D signatures under varying poses and lighting changes by using 3D signatures as a coupling term. The experiments show that this method can improve recognition performance of existing state-of-the-art wavelet signatures used in 3D face recognition and provide excellent recognition results in the 3D-2D face recognition application.