3D ear recognition using global and local features

This paper proposes a method for human recognition based on 3D ears and makes two important contributions. First, it proposes a global 3D descriptor, and second, it proposes a strategy to combine local and global descriptors for superior recognition performance. The proposed global descriptor is computed as follows. Spheres of different radii are placed at each point p in the point cloud, considering it as a center. A histogram is computed from the number of points falling in the annular regions of the concentric spheres. By utilizing the histograms of point p and its neighbors in a ring of fixed radius, an encoded value is obtained and is used to construct a coded image. This image is further divided into blocks which are subsequently binned to a histogram. Finally, the global descriptor is computed by concatenating all the histograms. The combined representation of four popular local 3D descriptors and the proposed global descriptor, yields superior results as compared to the case when local and global descriptors are used alone. The proposed technique has been validated on University of Notre Dame (UND), collection-J2 database and on our in-house database and has achieved a rank-1 recognition rate of 98.69% and 98.90%, respectively.