Geometric statistics-based descriptor for 3D ear recognition

Abstract

Several feature keypoint detection and description techniques have been proposed in the literature for 3D shape recognition. These techniques work well in discriminating different classes of shapes; however, they fail when used for comparing highly similar objects such as 3D ear or face in biometric applications. In this paper, we propose an efficient feature keypoint detection and description technique using geometric statistics for representation and matching of highly similar 3D objects and demonstrate its effectiveness in 3D ear-based biometric recognition. To compute the descriptor, we first extract feature keypoints from the 3D data by making use of surface variations followed by defining a descriptor vector for each keypoint. The descriptor vector is generated using three components. To compute the first component, concentric spheres that divide the space around a keypoint into annular regions are considered. Points falling in the annular regions are projected onto a plane perpendicular to the oriented normal of the keypoint. Lower-order moments of the 2D histogram of the spatial distribution of these projected points for each annular region are computed and used to define the first component of the descriptor vector. Next, component of the descriptor vector is computed using histograms of the inner products of the normals of the keypoint and its neighbours. The third component of the descriptor vector encodes the signed distances of the neighbours of the keypoint from the projection plane. Before concatenating individual components of the descriptor vector, the values are normalized to a common scale. Experiments on University of Notre Dame public database-Collection J2 (UND-J2) have achieved a rank-1 and rank-2 identification rates of $98.60\%$ and $100\%$, respectively, with an equal error rate of $1.50\%$. Comparative results show the superiority of the proposed descriptor in recognizing highly similar objects like human ear.