Abstract
Most existing ICP (Iterative Closet Point)-based 3D ear recognition approaches resort to the coarse-to-fine ICP algorithms to match 3D ear models. With such an approach, the gallery-probe pairs are coarsely aligned based on a few local feature points and then finely matched using the original ear point cloud. However, such an approach ignores the fact that not all the points in the coarsely segmented ear data make positive contributions to recognition. As such, the coarsely segmented ear data which contains a lot of redundant and noisy data could lead to a mismatch in the recognition scenario. Additionally, the fine ICP matching can easily trap in local minima without the constraint of local features. In this paper, an efficient and fully automatic 3D ear recognition system is proposed to address these issues. The system describes the 3D ear surface with a local feature—the Local Surface Variation (LSV), which is responsive to the concave and convex areas of the surface. Instead of being used to extract discrete key points, the LSV descriptor is utilized to eliminate redundancy flat non-ear data and get normalized and refined ear data. At the stage of recognition, only one-step modified iterative closest points using local surface variation (ICP-LSV) algorithm is proposed, which provides additional local feature information to the procedure of ear recognition to enhance both the matching accuracy and computational efficiency. On an Inter®Xeon®W3550, 3.07 GHz work station (DELL T3500, Beijing, China), the authors were able to extract features from a probe ear in 2.32 s match the ear with a gallery ear in 0.10 s using the method outlined in this paper. The proposed algorithm achieves rank-one recognition rate of 100% on the Chinese Academy of Sciences’ Institute of Automation 3D Face database (CASIA-3D FaceV1, CASIA, Beijing, China, 2004) and 98.55% with 2.3% equal error rate (EER) on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2, University of Notre Dame, South Bend, IN, USA, between 2003 and 2005).
Highlights
IntroductionEar based human recognition technology is a novel research field in biometric identification
Ear based human recognition technology is a novel research field in biometric identification.Compared with classical biometric identifiers such as fingerprints, faces, and irises, using an ear can has its distinctive advantages
To evaluate the robustness of our ear recognition approach to occlusions, we considered statistics statistics regarding the performance on subjects with occlusions in the University of Notre Dame (UND)-J2 and Chinese Academy of Sciences’ Institute of Automation (CASIA) databases
Summary
Ear based human recognition technology is a novel research field in biometric identification. Compared with classical biometric identifiers such as fingerprints, faces, and irises, using an ear can has its distinctive advantages. The collection of ear images is deemed to be easy and non-intrusive. Ear biometrics has recently received some significant attention. Researchers developed several approaches for ear recognition based on 2D ear images in the early years [2,3,4]. From those works, researchers found that the performances of 2D ear recognition methods were greatly affected by the pose variation and imaging condition.
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