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 LogGabor 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...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Cheng Zhong, Zhenan Sun, Tieniu Tan, Zhaofeng He
• Keywords: Casia 3d Face, Computer Vision, Current 3d Face, CVPR 2008, Data Noises, Face Recognition Performance, Large Expression Subset