Robust 3D face recognition using learn correlative features

3D images provide several advantages over 2D images for face recognition, especially when considering expression variations. In this paper, a novel framework is proposed for 3D-based face recognition. The key idea in the proposed algorithm is a correlative feature representation of the facial surface, by what is called 3D Local Binary Patterns (3D LBP), which encode relationships in neighboring mesh nodes and own more potential power to describe the structure of faces than individual points. The signature images are then decomposed into their principle components based on Spectral Regression resulting in a huge time saving. Our experiments were based on the CASIA 3D face database. Experimental results show our framework provides better effectiveness and efficiency than many commonly used existing methods for 3D face recognition and handles variations in facial expression quite well.