Robust 3D face recognition in uncontrolled environments

Most current 3D face recognition algorithms are designed based on the data collected in controlled situations, which leads to the un-guaranteed performance in practical systems. In this paper, we propose a Robust Local Log-Gabor Histograms (RLLGH) method to handle the uncontrolled problems encountered in 3D face recognition. In this challenging topic, large expressions and data noises are two main obstacles. To overcome the large expressions, we choose Log-Gabor features (LGF) to extract the distinctive and robust information embedded in 3D faces, which will be represented as 3D Log-Gabor faces. Data noises are summarized as distorted meshes, hair occlusions and misalignments. To overcome these problems, we introduce a robust local histogram (RLH) strategy, which takes advantage of the robustness of the accurate local statistical information. The combination of LGF and RLH leads to RLLGH. The novelties of this paper come from 1) Our work aims at studying 3D face recognition performance in uncontrolled environments; 2) We find that embedding LGF into the LVC framework leads to robustness in handling large expression variations; 3) The RLH strategy gives a promising way to solve the data noises problem. Our experiments are based on the large expression subset in FRGC2.0 3D face database and the expression subset in CASIA 3D face database. Experimental results show the efficiency, robustness and generalization of our proposed method.