Representation Plurality and Fusion for 3D Face Recognition

In this paper, the authors present an extensive study of 3D face recognition algorithms and examine the benefits of various score, rank and decision-level fusion rules. They investigate face recognizers from two perspectives: the data representation techniques used, and the feature extraction algorithms that match best each representation type. They also consider novel applications of various feature extraction techniques such as Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Non-negative Matrix Factorization (NMF), and principal curvature directions to the shape modality.