3D Ear Normalization and Recognition Based on Local Surface Variation

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 and2005).

Local and Holistic Feature Fusion for Occlusion-Robust 3D Ear Recognition

“…For the th i strategy from set Pk S , we calculate the earnings between it and each of the strategies from ' S by using Equation (8) and concatenate all of the earnings to a set. The number of elements in this set that are larger than zero is represented as i ne .…”

“…In the final classification stage, they resort to a sparse-representation-based Symmetry 2018, 10, 565 5 of 24 classification approach. Zhang et al [8] proposed an automatic 3D ear recognition method. First, a Faster R-CNN framework [26] is used to extract a rectangular box containing an ear image.…”

“…This detector was chosen because it is fully automatic, fast, and robust against occlusion and has been reported to yield an accuracy of 100% for the UND-J2 profile face database with 1800 images of 415 subjects [14]. The corresponding 3D data were then extracted from the coregistered 3D profile data, as described in [8].…”

“…Although a sharp increase in the size of the voxel can reduce the impact of the outlier [11], the experimental results in [11] show that a low recognition rate is only obtained in that case. To decrease the impact of outliers, some holistic-registration-based ear measurement methods [8] remove abnormal points using the distance threshold approach. Before the distance threshold is obtained, these methods need to build a correspondence between the surface points of the two models.…”

“…To the best to our knowledge, the best recognition rate is presented in [8], where the similarity of the components of each ear pair is computed on the basis of a modified ICP using the local surface variation (ICP-LSV) algorithm and the location of the ear pit. Related experimental results have shown that this method can achieve recognition accuracies of 94.2% and 97.6% when the ear is respectively obscured by minor amounts of hair and jewelry in relatively fixed positions [8]. However, when the ear image contains randomly positioned larger occlusions, the ear pit may not be visible, and the method becomes susceptible to paralysis.…”