# Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration
## 4 Experiments and Analysis
### Decision Layer Experiment  
The decision layer experiment was mainly focused on the experiment of illumination and explored the impact of decision methods on iris certification based on test statistics according to external lighting conditions. The comparison algorithms directly used the enhanced iris image for identification. The JLU-3.5 iris library of Jilin University was taken as the experimental iris library. The algorithm (Ensemble model) in this study's feature recognition layer was compared with the following cases:  
1. The feature recognition layer is composed based on the comparative experiment 1-5 in Section 4.2. The decision is made by most decision voting methods [44]; (Posterior probability decision)  
2. The feature recognition layer is composed based on the comparative experiment 1-5 in Section 4.2. The weight ratio is set for each recognition algorithm by test experiment, and the method of cumulative majority vote is used [45]; (Cumulative sums and majority vote)  
3. Multimodal ocular biometric in visible spectrum based on posterior probability method [46]. (Posterior probability decision)  
The number of comparisons in the iris library is shown in Table 5. The CRR and EER are shown in Table 6. The ROC curve of JLU-3.5 is shown in Figure 14. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Decision Voting**} & \multicolumn{3}{c}{**Posterior Probability**} & \multicolumn{2}{c}{**Cumulative Sums and**} & \multirow{2}{*}{**Ensemble Model**} \\  & & \multicolumn{2}{c}{**Decision**} & & & **Majority Vote** & \\ \hline CRR & EER & CRR & EER & CRR & EER & CRR & EER \\ \hline
98.42\% & 1.81\% & 97.83\% & 2.64\% & 95.76\% & 4.54\% & 99.18\% & 0.72\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The CRR and EER in decision layer experiment. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Category** & **Iris Images in** & **Total Number** & **Matches within** & **Matches outside** & **Total Number** \\
**Number** & **per Category** & **of Images** & **Category** & **Category** & **of Matches** \\ \hline
100 & 20 & 2000 & 2645 & 8156 & 10801 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Number of comparisons in decision layer experiment. In the decision layer experiment, as can be seen from the results of Figure 14 and Table 6: the certification structure of this paper has the highest recognition rate, the lowest EER, and the ROC curve is closer to the horizontal and vertical axes. Although the image processing layer and the feature recognition layer can effectively eliminate the blurred image and the influence of the deflection, the illumination effect is difficult to exclude, because the fixed threshold cannot accurately quantify the influence of the illumination, so the intensity of illumination can affect the appearance of iris features to a certain extent, and can also affect the amount of information in a certain area. The recognition accuracy of the decision-level fusion strategy algorithm, which causes the posterior probability iris, sclera and periocular features is also be affected. Most voting methods are applicable to the case where there are many recognition algorithms, which will slow down the speed of certification. At the same time, through the feature recognition layer experiment, it can be seen that single recognition algorithms also received interference from the external environment, which may be a leak in the recognition of voting. The cumulative voting method based on the weight ratio is dependent on the setting of the feature ratio. Both cumulative voting and majority voting can potentially take the occasional case as statistical data, which may lead to identification error. In this paper, the decision layer adopted a method of trusted analysis. The statistical algorithm is recognized under different illumination conditions, and then it is analyzed which algorithm is more reliable in one certain specific illumination condition.