Current growth trends in different biometrics applications present challenges to researchers. To address these challenges, we need new data storage and retrieval techniques to make the recognition process time efficient. This paper presents a system for time efficient 3D ear biometrics from a large biometrics database. The proposed system has two components that are primarily responsible for: 1) automatic 3D ear segmentation and 2) hierarchical categorization of the 3D ear database using the shape information and surface depth information, respectively. We use an active contour algorithm along with a tree-structured graph to segment the ear region from the 3D profile images. The segmented 3D ear database is then categorized based on the geometrical feature values, computed from the ear shape, into oval, round, rectangular, and triangular categories. For the categorization based on the depth information, the feature space is partitioned using tree-based indexing techniques. We used indexing techniques with balanced split (k-dimensional (KD) tree) and unbalanced split (pyramid tree) data structures to categorize the database separately and then compared their retrieval efficiency. Experiments are conducted to compare the average computation time per query when performing recognition through hierarchical categorization with the average computation time when recognition is based on sequential search. Experimental results without indexing conducted on the University of Notre Dame Collection J2 data set yielded a rank-one recognition rate of 98.5%. We applied the indexing technique to compute the rank-one recognition accuracy with a 10%-50% search space reduction using a 10% step size. With 10%, 20%, 30%, 40%, and 50% search space reduction the rank-one recognition accuracy gracefully degrades to 96.87%, 96.14%, 95.18%, 94.21%, and 93.49%, respectively, while performing nearly 3, 3.3, 4, 4.2, and 5 times faster than the state-of-the-art technique that uses sequential search.