# Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration
## 4 Experiments and Analysis
### Feature Recognition Layer Experiment  
The feature recognition layer experiment was mainly used for testing iris deflection, and explored the effect of iris deflection on iris certification. Except for the algorithm in this paper, the other comparison algorithms directly use enhanced images for recognition, and do not provide a decision layer. The JLU-6.0 iris library of Jilin University was taken as the experimental iris library. The number of comparisons in the iris library is shown in Table 3. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No Processing**} & \multirow{2}{*}{**Gaussian Filtering**} & \multicolumn{3}{c}{**Log Operator and Equalization**} & \multirow{2}{*}{**Ensemble Model**} \\  & & & & & & **Histogram** & \\ \hline CRR & EER & CRR & EER & CRR & EER & CRR & EER \\ \hline
97.45\% & 2.88\% & 98.76\% & 1.43\% & 99.03\% & 0.89\% & 99.41\% & 0.48\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The CRR and EER in image processing layer experiment. Figure 12: The ROC curve of JLU-5.0: (**a**) Global map; and (**b**) Partial magnification. The algorithm (Ensemble model) in this paper's feature recognition layer was compared with the following cases:  
1. The iris recognition algorithm based on Zernike moment phase feature [40];
2. The iris recognition algorithm based on deep learning architecture [41];
3. The iris recognition algorithm based on statistically characteristic center symmetric local binary pattern (SCCS-LBP) [42];
4. The secondary iris recognition algorithm based on BP neural networks [11];
5. The iris recognition algorithm based on cross-spectral matching [43];
6. Iris recognition based on the Gabor + Hamming algorithm in the recognition layer of this study;
7. Iris recognition based on the Haar + BP algorithm in the recognition layer of this study. The CRR and EER in the eight algorithms are shown in Table 4. The ROC curve of JLU-6.0 is shown in Figure 13. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Zernike**}} & \multicolumn{3}{c}{**Deep Learning**} & \multicolumn{3}{c}{**SCCS-LBP**} & \multicolumn{2}{c}{**Gabor + Hamming**} \\ \hline CRR & EER & CRR & EER & CRR & EER & CRR & EER \\ \hline
98.73\% & 1.47\% & 96.78\% & 3.58\% & 97.43\% & 2.63\% & 97.79\% & 2.31\% \\ \hline \multicolumn{1}{c}{
\begin{tabular}{c} **Secondary Iris** \\ **Recognition** \\ \end{tabular} } & \multicolumn{3}{c}{**Cross-Spectral Matching**} & \multicolumn{3}{c}{**Ensemble Model**} & \multicolumn{3}{c}{**Haar + BP**} \\ \hline CRR & EER & CRR & EER & CRR & EER & CRR & EER \\ \hline
99.14\% & 0.84\% & 97.98\% & 2.05\% & 99.48\% & 0.57\% & 98.18\% & 1.79\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The CRR and EER in feature recognition layer experiment. Figure 13: The ROC curve of JLU-6.0: (**a**) Global map; (**b**) Partial magnification. \begin{table}
\begin{tabular}{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
50 & 100 & 5000 & 8765 & 20623 & 29388 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of comparisons in feature recognition layer experiment. In the feature recognition layer experiment, the results of Figure 14 and Table 4 are analyzed by the certification situation of the same person with the deflected iris: the certification accuracy of the certified structure is the highest, the EER is the lowest, and the ROC curve is closer to the horizontal and vertical coordinates axes. This is because, after filtering the texture information through the image processing layer, it is necessary to express the features of the iris according to the change rule of the amount of information of each part.