Robust sparse bounding sphere for 3D face recognition

摘要

A robust sparse bounding sphere representation (RSBSR) is proposed to analyze 3D facial data. There are many obstacles to distinguishing facial differences, such as large pose and expression variations, hair occlusions and noise corruptions. In our framework, 3D point clouds are first preprocessed to remove the irrelevant areas and to align with a frontal neutral face model for overcoming the influence of large pose variations based on axis-angle representation. Then, 3D facial models are projected on the bounding spheres to describe both the depth and 3D geometric shape information, referred to as bounding sphere representation (BSR). This descriptor has the potential of decreasing the influence of large expression and pose variations on each normalized face within the corresponding spherical domain. Next, a robust group sparse regression model (RGSRM) is proposed to estimate the regression matrix, which preserves the intrinsic discriminant information. By embedding the descriptors into the low dimensional regression matrix, hair occlusions and artifacts can be treated as corruptions and can be patched. Under the constraints of Spectral Regression and corruptions, noise corruptions can be removed and the remaining small variations can be further corrected. FRGC v2.0 and CASIA 3D face databases are introduced to examine the performance of our framework and the previous algorithms with different schemes, and the experimental results show our proposed framework has the performance of simple implementation, high accuracy and low computational complexity.