# Individuals Identification Based on Palm Vein Matching under a Parallel Environment
## 5 Results
### Impact of the Sparse Matching on the Time Efficiency  
One of the main contributions of our approach is to reduce the execution time of the dense correspondences between palm vein images; in this experiment, we analyze the impact of the sparse matching on the time efficiency of the recognition pipeline. Thus, we propose using CPM as sparse matching algorithm, that as we have seen in previous experiments, it achieves a good performance in verification and identification tasks. Now, we examine the time efficiency of our proposal and also we compare its results against the baseline method proposed in [45]. In all cases, the reported results were obtained by averaging 10 repetitions of 1200 similarity tests on CASIA-MS-PalmprintV1 database, due to \begin{table}
\begin{tabular}{c c c} \hline \hline
**Database** & **Rank-One Recognition Rate** & **Lowest Rank of Perfect Recognition** \\ \hline CASIA-MS-PalmprintV1 & 0.9809 (\(\pm\)0.0049) & 31 \\ VERA Palm vein & 0.9914 (\(\pm\)0.0037) & 26 \\ PUT Vein & 0.9953 (\(\pm\)0.0031) & 19 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the proposed method on the databases in identification mode. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Database** & **Templates** & **Similarity Tests** \\ \hline \multirow{2}{*}{CASIA-MS-PalmprintV1} & 200 & 120,000 \\  & (100 _persons_\(\times\) 2 _hands_) & (100 _persons_\(\times\) 2 _hands_\(\times\) 3 _testing samples_\(\times\) 200 _templates_) \\ \hline \multirow{2}{*}{VERA Palm vein} & 220 & 242,000 \\  & (110 _persons_\(\times\) 2 _hands_) & (110 _persons_\(\times\) 2 _hands_\(\times\) 5 _testing samples_\(\times\) 220 _templates_) \\ \hline \multirow{2}{*}{PUT Vein} & 100 & 60,000 \\  & (50 _persons_\(\times\) 2 _hands_) & (50 _persons_\(\times\) 2 _hands_ 6 _testing samples_\(\times\) 100 _templates_) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Number of templates and similarity tests for the experiments in identification mode on each database. Figure 9: Cumulative match curves on database: (**a**) CASIA-MS-PalmprintV1; (**b**) VERA Palm vein; and (**c**) PUT Vein. this database being more challenging than the other two. The execution times of each process of the recognition pipeline are listed in Table 7, and Figure 10 studies the effect of our multi-core implementation on the matching process. As it can be seen in Table 7, the overall speed-up obtained by our method with respect to the baseline is above 15\(\times\). Mainly, this result is a consequence of the reduction on the execution time of the sparse matching process introduced in our proposal, which is 0.39 ms while the dense matching of the baseline method is 10,038 times higher. However, it is worth mentioning that the execution time of the feature extraction of our approach is five times higher than the baseline. This behavior contrasts with the results reported by [57], who found that the DAISY descriptor can be computed more efficiently than SIFT. It can be explained by the fact that we used the algorithm implementation of OpenCV library [64] for feature extraction, which are not the same as those used in their experiments. Nevertheless, the DAISY algorithm is suitable to be parallelized under multi-core and GPU platforms, which we will study in the future in order to improve much more the time efficiency of our method. Given the results depicted in Figure 10, we can say that the master-worker scheme implemented on the matching process linearly increases the number of queries per second computed by our method with varying number of cores. Contrarily, the baseline algorithm shows smaller growth with respect to the increase in the number of processing cores. The main reason is that CPM enables a very efficient memory access by using the DAISY descriptor instead of SIFT, which greatly improves efficiency. In addition, this is possible by the fact that the random search with the coarse-to-fine scheme on a sparse image grid structure is more computationally efficient than dense correspondences implemented in the baseline algorithm.