# 3D Ear Normalization and Recognition Based on Local Surface Variation
## 5 Conclusions  
An efficient and fully automatic 3D ear recognition system is proposed in this paper which consists of three components: ear region segmentation, ear data normalization, and ear recognition.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Procedure** & **Detection** & **Gridfit Processing** & **LSV Extraction** & **Normalization** & **Matching** & **Total** \\ \hline Time Cost & 0.22 & 0.03 & 2.32 & 0.05 & 0.1 & 2.72 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The computational time cost of the proposed system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Data Preprocess Algorithm** & \begin{tabular}{c} **Recognition** \\ **Algorithm and** \\ **Database** \\ \end{tabular} & \
\begin{tabular}{c} **Rank-One** \\ **Recognition** \\ **Rate (\%)** \\ \end{tabular} & **EER (\%)** \\ \hline \multirow{3}{*}{[PERSON] [6]} & Manual extraction and ear &
\begin{tabular}{c} Key points \\ matching \\ UND 415 object \\ \end{tabular} & 95.1 & 4 \\ \hline \multirow{2}{*}{[PERSON] [32]} & Edge map and \\  & connected components & SURF + GPA-ICP \\  & & UND 404 object \\ \hline \multirow{2}{*}{[PERSON] [17]} & Ear pit location ACM & ICP \\  & & UND 415 object \\ \hline \multirow{2}{*}{[PERSON] [11]} & Edge extraction Reference & LSP + ICP \\  & Ear Shape Model & UND 302 object \\ \hline \multirow{2}{*}{[PERSON] [12]} & \multirow{2}{*}{Adaboost} & L3 DF + ICP \\  & & UND 415 object \\ \hline \multirow{2}{*}{This paper} & Adaboost and ear & ICP-LSV \\  & normalization & UND 415 object \\ \hline \hline \end{tabular}  
* SURF, Speed Up Robust Feature; GPA-ICP, integration of Generalized Procrustes Analysis with; ACM, Active Contour Model; ISP, Local Surface Patch; 13 DF, Local 3D Features.  
\end{table}
Table 5: A comparison of the proposed approach with similar approaches 1.  
The ear regions were cropped from 2D profile images by training a Faster R-CNN model. Then a procedure of ear data normalization was proposed to eliminate most of the background noise data and provide the iteration algorithm a refined ear data. In the ear recognition procedure, a 3D ear matching scheme using only one-step ICP-LSV algorithm was proposed which brings additional local feature information into global registration. The initially rough alignment of gallery-probe pair is not necessary in the proposed approach. The experimental results demonstrate that the proposed ear recognition system outperforms other state-of-the-art ICP-based 3D ear recognition systems.  
It was found that, in the case of large pose variations or serious hair occlusions, the performance of the proposed approach will be influenced. Therefore, we intend to utilize the block-based matching algorithm to resolve these problems in future work.  
This article was supported by the National Natural Science Foundation of China (Grant No. 61472031), the National Natural Science Foundation of China (Grant No. 61300075), Beijing Higher Education Young Elite Teacher Project (Grant No.YETP0375), and the Fundamental Research Funds for the Central Universities under the Grant No. FRF-TP-14-120A2. Portions of the research in this paper use the CASIA-3D FaceV1 database collected by the Chinese Academy of Sciences' Institute of Automation (CASIA). The authors would like to thank the computer vision research laboratory at University of Notre Dame for providing their biometrics databases. The authors also want to thank [PERSON] for his help. [PERSON] and [PERSON] conceived and designed the experiments. [PERSON] performed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] wrote the paper. [PERSON] and [PERSON] reviewed and edited the manuscript. All authors read and approved the manuscript.  
The authors declare no conflict of interest.