# 3D Ear Normalization and Recognition Based on Local Surface Variation
## 1 Introduction  
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. An ear has a stable and rich structure that changes little with age and does not suffer from changes in facial expressions at the same time [1]. Moreover, the collection of ear images is deemed to be easy and non-intrusive. As such, 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. Compared with 2D earimages, the 3D ear data are relatively insensitive to illuminations and posture variation. Therefore, ear recognition methods utilizing 3D shape information have become a recent trend in research field [5; 6; 7; 8; 9; 10]. Most existing 3D ear recognition approaches are based on the ICP (Iterative Closet Point) algorithm. Although ICP is thought to be the most accurate matching algorithm, it requires concise ear data and a good initial rigid transformation to ensure global convergence. Researchers have proposed several two-step ICP based matching techniques to register probe and gallery ear data [11; 12]. In these methods, local surface descriptors were used to extract a set of key points on the ear surface which were only employed to estimate the initial rigid transformation between a gallery-probe pair. The fine matching was based on the entire ear region data. However, these methods were notably limited by the redundancy data and the computation load of a two-step ICP algorithm. The ear region data utilized in most of the existing methods were coarsely segmented from profile images, so there was a mass of non-ear data, such as flat face skin data and hair data. It is essential to refine and normalize the ear data since any extra data can lead to mismatching in the ICP algorithm. In this work, instead of being used to extract key points, the local feature LSV (Local Surface Variation) is utilized to eliminate redundancy data from ear regions. The LSV is responsive to the concave and convex areas of the surface, so that the flat face skin data will be removed if the appropriate threshold value of LSV is selected. Furthermore, most of the hair data are removed and the ear pose is normalized via the proposed template matching methods. Therefore, the computation load of the ICP algorithm markedly drops down because the size of the normalized ear data is one third the size of the original data size. With the ICP algorithm it is easy to fall into local minimum values without the constraint of local features. As such, the existing two-step ICP based matching techniques roughly align the gallery-probe pairs using key points during the coarse matching. However, the two-step ICP algorithm can be extremely time-consuming. To combine the local feature matching and global registration in the ICP algorithm, a modified ICP algorithm named ICP using Invariant Features (ICPIF) was proposed by [PERSON] [13]. Compared with traditional ICP, the corresponding points are selected according to a weighted linear combination of positional and feature distances in the ICPIF algorithm. It has been demonstrated that the ICPIF converges to the minimum distance with less iterations than the traditional ICP algorithm. Maximum and minimum principal curvatures are perhaps the most common invariant. However, it would increase the computation load to bring two feature distances into the iteration. The proposed recognition procedure obtains a better performance than the traditional coarse-to-fine registration algorithm utilizing a one-step modified ICP algorithm.