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A robust regional bounding sphere representation (RRBSR) is introduced to facilitate 3D face recognition. In our framework, we first segment a group of regions on each 3D facial point cloud by curvature information and facial shape characteristics. Then, the extracted regions are projected on the bounding spherical bands in order to reflect the distinctive shape information of facial different regions. Next, an orthogonal regional and global regression (ORGR) is utilized to extract the discriminant feature vectors. Experimental results based on the different 3D face databases demonstrate that balancing regional and global facial characteristics allow for the high-qualified performance. Compared with the previous popular approaches, our framework has a consistently better performance in terms of effectiveness, robustness, and universality. |
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Keywords:regional bounding sphere representation; 3D face recognition; orthogonal regional and global regression |
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