# 3D Ear Normalization and Recognition Based on Local Surface Variation
## 2 Related Work and Contributions
### 3D Ear Recognition
The similarities for all model-test pairs were computed using the LSP featuresand ranked using SVM to generate a short list of candidate models for verification. The verification was performed by aligning a model with the test object via the ICP algorithm. On the UND Collection F, the rank-one recognition rate was 96.7%, and EER was 1.8%. In a study by [PERSON] et al. [PERSON] et al. (2017), a coarse-to-fine hierarchical technique was used where the ICP algorithm was first applied on low and then on high resolution meshes of 3D ear data. The rank-one recognition rates of 93% and 93.98% were achieved respectively on UND Biometrics Database A and Database B. In a later approach [PERSON] et al. (2017), [PERSON] represented the 3D ear data with local 3D features (L3 DF) to extract a set of key points for L3 DF-based initial alignment, then the fine recognition result was obtained through the ICP algorithm. The system provided an identification rate of 93.5% on the UND-J database. Nevertheless, extraction of L3 DF was relatively complex, so that the extraction time for a single ear was 22.2 s on Inter\({}^{\text{\textregistered}}\) CoreTM2 Quad 9550, 2.83 Gigahertz (GHz) machine. [PERSON] and [PERSON] proposed a two-step matching technique which makes use of 3D and co-registered 2D ear images [PERSON] and [PERSON] (2018). They extracted a set of local 2D features points using the Speed Up Robust Feature (SURF) descriptor. Then the co-registered salient 3D data points were used to coarsely align 3D ear points. The final matching was performed by integration of Generalized Procrustes Analysis (GPA) with ICP (GPA-ICP). The technique achieved a verification accuracy of 98.30% with an EER of 1.8% on the UND-J2 database. The key points were extracted from the 2D ear images--compared with 3D local descriptors, the SURF descriptor may not be robust to pose and illumination variations. The schemes based on local feature matching and ICP global registration both have advantages and disadvantages for ear recognition. The matching method using local surface descriptors can represent free-form surfaces effectively, but it may cause mismatching within similar key points without global constraint. Although the ICP-based algorithm obtains high accuracy regarding the registering of 3D rigid models, it may be trapped in local minimum without an accurate initial rigid transformation. It is clear that the combination of local feature matching and ICP global registration in 3D ear recognition is effective. However, in the existing two-step alignment methods, the local feature points are extracted to refine the initial alignment, and then the results of matching are based on the ICP procedure using the original ear data. It treats all the points equally, regardless of how much useful information the point represents. However, it ignores the fact that not all the points in the coarsely segmented ear data make positive contributions to recognition. Since the local features can distinguish the useful data points from useless data, we do not have to utilize the original data to make the final decision according to the ICP-based matching.