# Individuals Identification Based on Palm Vein Matching under a Parallel Environment
## 5 Results
### Comparison with State-of-the-Art Approaches  
To conclude, the recognition performance obtained by our approach is compared to state-of-the-art approaches with experimentation on CASIA-MS-PalmprintV1 database. We select this database to compare state-of-the-art approaches because it is widely used in the literature and it is the most relevant dataset for palm vein recognition. Furthermore, it should be noticed that some works report their experimental results on self-created datasets, with different measures or different experimental scenarios, which do not allow for making an equivalent comparison. For this reason, recent approaches such as [43; 44] based \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **EER** & **Rank-One Recognition Rate** & **Computation Time (ms)** \\ \hline CPM [52] & 0.0072 & 0.9651 & 0.39 \\ DeepMatching [51] & 0.0268 & 0.9549 & 501.39 \\ SparseFlow [49] & 0.0391 & 0.9267 & 635.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of results of different sparse matching methods on a CASIA-MS-PalmprintV1 database. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Year** & **Methodology** & **EER** \\ \hline [PERSON] et al. [38] & 2014 & RootSIFT and Euclidean distance & 0.00996 \\ [PERSON] and [PERSON] [28] & 2014 & Improved LBP on Mutual foreground & 0.0236 \\ [PERSON] et al. [41] & 2015 & Score level fusion with SIFT and ORB with bi-directional matching & 0.0073 \\ [PERSON] [34] & 2015 & Feature Extraction using Partial Least Square and Euclidean distance & 0.0133 \\ [PERSON] et al. [29] & 2016 & LDTP and Chi-square dissimilarity test & 0.03 \\ [PERSON] et al. [68] & 2016 & Local Binary Pattern Rotation Invariant and Cosine distance & 0.1170 \\ Proposed method & 2019 & CPM with Daisy descriptor and Displacement uniformity texture & 0.0072 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of our method with other methods of the state-of-the-art. on CNN are not compared in this section. Table 9 summarizes the results of EER achieved by different methods which have been published recently. In order to provide a statistical analysis of the results respect to state-of-the-art, we followed the recommendations pointed out by [PERSON] [69] and the extensions presented in [70] related to the computations of adjusted \(p\)-values. First, we applied the non-parametric Friedman-test to prove the null hypothesis that all the algorithms obtained the same results on average. Then, once the Friedman test rejected the null hypothesis, we applied post hoc tests. Specifically, the Bonferroni-Dunn test is applied to define if our method significantly outperforms the next ranked approach. In addition, we used the [PERSON]'s step-down procedure to complement the above multi-comparison statistical analysis. It should be noticed that the proposal of [69] recommends a set of non-parametric statistical tests and procedures which can be safely used for comparing the performance of more than two classifiers over multiple data sets. However, we belief it also is suitable to compare different approaches on a single dataset. It should be noticed that making a statistical analysis on palm vein recognition over multiple databases is very difficult because most of state-of-the-art approaches use different and/or self-created databases. Table 10 presents EER results reported by each approach and the respective ranking. In this case, we statistically analyzed the results to detect significant differences among compared approaches and their EER results. The Friedman'test rejected the null hypothesis with \(p=0.4232\). Then, we applied the post hoc Bonferroni-Dunn test at \(\alpha=0.10\) to detect which method performed equivalently to our approach, which is the best ranked.