Learning effective features for 3D face recognition

3D images provide several advantages over 2D images for face recognition, especially when considering expression variations. In this paper, a novel framework is proposes for 3D-based face recognition. The key idea in the proposed algorithm is a representation of the facial surface, by what is called a Bending Invariant (BI), invariant to isometric deformations resulting from expressions and postures. In order to encode relationships in neighboring mesh nodes, Gaussian-Hermite moments are used for the obtained geometric invariant, which is a richer representation, due to their mathematical orthogonality and effectiveness in characterizing local details of the signal. The signature images are then decomposed into their principle components based on Spectral Regression Kernel Discriminate Analysis (SRKDA) resulting in a huge time saving. Our experiments are based on FRGC v2.0 face database. Experimental results show our framework provides better effectiveness and efficiency than many commonly used existing methods and handles variations in facial expression quite well.