# Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration
## 4 Experiments and Analysis
### Image Processing Layer Experiment  
The image processing layer experiment is mainly focused on the influence of iris texture on iris certification under defocused conditions. A recognition algorithm and decision layer were adopted in this paper's algorithms. The experimental iris library was the JLU-5.0 iris library.  
The number of comparisons in the iris library is shown in Table 1.  
Compared the algorithm (ensemble model) in this paper's image processing layer with these following cases:  
1. Do not process the iris, test iris recognition without noise suppression; (No processing)
2. Only use Gaussian filter to treat the iris, test iris recognition in the case of a single noise suppression algorithm; (Gaussian filtering)
3. The Log operator [32] and equalization histogram are used to process the iris, and the iris recognition under the combination of algorithms will be tested. (Log Operator and Equalization Histogram)  
The CRR and EER in the four cases are shown in Table 2. The ROC curve of JLU-5.0 is shown in Figure 12.  
In the image processing layer experiment, as can be seen from the results of Figure 12 and Table 2, the iris image processed in this paper has the highest recognition rate and the lowest EER after iris feature  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Iris Images** & **Total Number** & **Matches within** & **Matches outside** & **Total Number** \\
**Number** & **per Category** & **of Images** & **Category** & **Category** & **of Matches** \\ \hline
100 & 10 & 1000 & 1600 & 5130 & 6730 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of comparisons of image processing layer experiment.  
extraction and recognition. This is because the image processing layer is designed for feature recognition services, which itself is used to preserve the feature points in the iris texture as much as possible, and then reflect the iris features according to the change rule of the amount of information of different regions.  
In the constrained iris, the blurred iris makes the definition between the texture and the non-texture unclear, and the effective feature information will be filtered, which can cause the change in the amount of information to be unrealistically reflected. Therefore, the filter algorithm of the image processing layer has a positive significance for the feature expression of the blurred image. The algorithm can filter out the non-textured regions, leaving the purest. A few image processing algorithms can also extract enough effective iris information to identify and improve the accuracy. However, too few processing algorithms are not representative, which is not conducive to find the feature points inside the iris; however, for many filter algorithms, the feature processing is too cumbersome, and the iris texture is also reduced. In this algorithm, six different angles of iris image processing algorithms are used to maximize the effective iris information and remove redundant parts to improve the recognition accuracy.  
However, due to the algorithm's inability to fully control the external environment and the iris deflection problem, the effect of improvement may be limited.