Expression-robust 3D Face Recognition using Bending Invariant Correlative Features

In this paper, a novel 3D Bending Invariant Correlative Features (3D BI-LBP) is used for 3D face recognition to overcome some of the unsolved problems encountered with 3D facial images. In this challenging topic, large expression and pose variations along with data noise are three major obstacles. We first exploit an automatic procedure regarding face area extraction, and then process it to minimize the effect of large pose variations and effectively improve the total 3D face recognition performance. To overcome the large expression variations, the key idea in the proposed algorithm is a representation of the facial surface, by what is called a Bending Invariant (BI), which is invariant to isometric deformations resulting from changes in expression and posture. In order to encode relationships in neighboring mesh nodes, 3D LBP is used for the obtained geometric invariant, which own more potential power to describe the structure of faces than individual points and effectiveness in characterizing local details of a signal. The signature images are then decomposed into their principle components based on Spectral Regression (SR) resulting in a huge time saving. Our experiments were based on the CASIA and FRGC 3D face databases which contain large expression and pose variations. Experimental results show our proposed method provides better effectiveness and efficiency than many commonly used existing methods for 3D face recognition and handles variations in facial expression quite well.