# 3D Ear Normalization and Recognition Based on Local Surface Variation
## 4 Experiments
### Comparison and Discussion  
In this section, the parameters of the LSV representation are evaluated, and then the comparisons of different algorithms are submitted. Finally, performance of this ear recognition system is analyzed with respect to other ICP-based recognition system. All the comparisons are based on the UND-J2 database. Figure 12 shows the recognition results with several neighborhood sizes and thresholds. Although the variation of rigid local surfaces is an inherent feature, the LSV value of a vertex changes with the scope of its neighborhood. Additionally, if the system modifies the thresholds of LSV values, the recognition performance is changed accordingly. It can be observed that the results with 3-ring neighborhood groups outperform the 2-ring and 4-ring groups. It can also be observed that the matching time and recognition results are reduced along with the increase of threshold value in every  
Figure 11: The examples of false recognition utilizing coarsely segmented ear data (**the top row**) and corresponding normalized ear data (**the bottom row**). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ear Data (from UND)** & **Matching Time (s)** & **Average Iterations** & **Recognition Rate (\%)** \\ \hline coarsely segmented data & 0.39 & 21.1 & 85.54 \\ Normalized data & 0.1 & 17.2 & 98.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of recognition result using different ear data. group. As shown in Figure 4, the procedure with higher threshold eliminates plenty of useful ear data to perform faster but less accurately. To find a balance between computational complexity and accuracy, the threshold of 0.01 and 3-ring neighborhood size were employed in the system. The iteration times of the ICP algorithm which is used for normalization are also evaluated in Figure 13. The result of the left most bar is obtained utilizing the ear data which are normalized in pose and position by the barycentric coordinates. The experimental results show that the coarsely segmented ear data and the ear template can be roughly aligned even using only a one-iteration ICP algorithm. This also proves that the ICP alignment in the normalization procedure is necessary and efficient. The times required for five iterations were determined according to the recognition performance. Table 4 displays the comparison of different algorithms using the same normalized ear data. It was experimentally proven that the ICP-LSV algorithm obtains a 4.2% higher recognition rate than the traditional ICP algorithm does on the normalized UND-J2 database. Although combining positional and feature distances in the iteration may increase the computation load of each iteration, the ICP-LSV algorithm converges to minimum distance with less iterations, as such, there is not a significant increase of the total matching time. The experimental results also demonstrated that the ICP-LSV  
Figure 12: The recognition performance with different neighborhood sizes and thresholds. Figure 13: The performance of Iterative Closet Point (ICP) on the UND-J2 database with different iteration times utilized for normalization. method performs better than the ICPIF method based on curvatures in the aspect of computation time and matching results. Figure 14 illustrates several groups of verification and identification results on the UND-J2 database. Therefore, we can intuitively understand the contribution of each step to the final result. Firstly, it is shown that all of the groups with normalized ear data obtained more satisfactory performances than the groups with coarse extracted ear data did. This means that the normalization part has made the most important contribution in the proposed method. Secondly, the groups based on traditional ICPIF algorithm and the proposed ICP-LSV algorithm performed better than the groups based on the original ICP algorithm. This demonstrates that the thought of combining local feature extraction and ICP global registration is correct.