# 3D Ear Normalization and Recognition Based on Local Surface Variation
## 2 Related Work and Contributions
### 3D Ear Recognition  
Existing 3D ear recognition approaches utilizing 3D point cloud or range images can be basically classified as local feature matching, ICP global registration, or a combination of both. In a previous study [6], [PERSON] et al. proposed a method to sort key points on point clouds for 3D ear shape matching and recognition. The Gaussian-weighted average of the mean curvature was utilized to select the salient key points. Then the angle between two feature vectors was used to calculate the similarity of two local features. Finally, the overall similarity of two ears was measured by the confidence weighted sum of all the measures. The approach achieved a rank-one recognition rate of 95.1% and an equal error rate (EER) of 4% on UND-J2 database. [PERSON] et al. proposed an ear recognition approach based on 3D key point matching in [25]. The 3D key points were detected using the shape index image and the scale space theory. Then they constructed 3D Center-Symmetric Local Binary Pattern (CS-LBP) features and used a coarse to fine strategy for 3D key point matching. The rank-one recognition rate on UND-J2 database was 96.39%. The ICP algorithm is widely used to align 3D rigid models [26]. The algorithm obtains correspondences by looking for the closest points, and then minimizes the mean square distance between the pairs. [PERSON] and [PERSON] [27] proposed an approach based on ICP for 3D ear recognition using video sequences. They obtained 84% rank-one recognition rate on a database of 61 gallery and 25 probe images. [PERSON] and [PERSON] compared three ear based human recognition techniques in [28]. They explored the use of a Principal Component Analysis (PCA)-based approach on a range image representation of the 3D data, Hausdorff matching on edge images obtained from 3D ear images and an ICP approach on a point-cloud representation of the 3D data. They confirmed that ICP matching achieved the best performance. In their later work [17], [PERSON] and [PERSON] put forward an approach of 3D ear recognition according to the RMS registration error of the ICP algorithm. They used a k-d tree data structure in the search for closest points and limited the maximum number of iterations to reduce the time consumption. The system achieved a rank-one recognition rate of 97.8% on the UND database in the identification stage and an EER of 1.2% in the verification stage. However, only beginning with a translation vector which was estimated from the ear pit location, it took 5-8 s to match a pair of ears on a dual processor 2.8 Gigahertz (GHz) Inter(r) Pentium Xeon system. Therefore, this indicated that an initial guess of the full 3D (translation and rotation) transformation is significant for an ICP-based algorithm. Recently, the coarse-to-fine ICP based 3D ear recognition algorithm which combines local feature extraction and ICP global registration has drawn extensive attention. The gallery-probe pairs are coarsely aligned based on the local information extracted from the feature correspondences in order to get a relatively accurate initial transformation, and then are finely matched via the ICP global registration. [PERSON] and [PERSON] detected and aligned the helix of the gallery-probe pairs to get the initial rigid transformation in [29]. Then the ICP algorithm iteratively refined the transformation to bring model ears and test ears into the best alignment. The recognition rate on a database of 30 subjects was 93.3%. In a previous study [11], [PERSON] and [PERSON] created the ear helix/antihelix representation and the local surface patch (LSP) representation, which were employed to estimate the initial transformation for a modified ICP algorithm separately. They obtained 96.03% and 96.36% recognition rates, respectively, on the Collection F of the UND database. Due to the high dimensionality of LSP feature representation, in their later approach [30], an embedding algorithm was employed to map the feature vectors to a low-dimensional space.