Rigid-area orthogonal spectral regression for efficient 3D face recognition

Abstract

A new framework is proposed for 3D face recognition, called Rigid-area Orthogonal Spectral Regression (ROSR). We utilize the depth images of 3D facial rigid area for efficiently discriminant feature extraction. The framework can effectively estimate the regression matrix to describe intrinsic facial surface features. Large expressions, treated as non-rigid transformations, along with data noise, are the major obstacles that significantly deteriorate the facial linear structure. In our framework, we first utilize the curvature information to remove the non-rigid areas in the 3D face images. Orthogonality can minimize the reconstruction errors and Spectral Regression can accurately describe the manifold structure of the samples. We take these advantages into consideration and propose the ROSR framework, employed for 3D face recognition. Additionally, regression analysis is much faster than the traditional methods. CASIA, Bosphorus and FRGC 3D face databases are introduced for experimental evaluation. Compared with the other commonly used algorithms, our framework has a consistently better performance in terms of efficiency and robustness.