An adaptive resolution voxelization framework for 3D ear recognition

Abstract

We present a novel voxelization framework for holistic Three-Dimensional (3D) object representation that accounts for distinct surface features. A voxelization of an object is performed by encoding an attribute or set of attributes of the surface region contained within each voxel occupying the space that the object resides in. To our knowledge, the voxel structures employed in previous methods consist of uniformly-sized voxels. The proposed framework, in contrast, generates structures consisting of variable-sized voxels that are adaptively distributed in higher concentration near distinct surface features. The primary advantage of the proposed method over its fixed resolution counterparts is that it yields a significantly more concise feature representation that is demonstrated to achieve a superior recognition performance. An evaluation of the method is conducted on a 3D ear recognition task. The ear provides a challenging case study because of its high degree of inter-subject similarity.