# Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration
## 4 Experiments and Analysis
### Certification Structure of Comprehensive Effect Experiment
#### 4.4.2 Existing Method Comparison Experiment  
The iris used in the comparative experiment is the iris recognition area (\(256\times 32\) dimension), and the iris recognition is shown in Table 7 (the same as in Section 4.4.1). The number of training irises is 20% of the number of test irises, and the training irises are not the same as the test irises. 1. The certification algorithm is compared with the following seven algorithms. 2. Concept cognition based on deep learning neural network [47], compared with deep learning architecture cognitive model for a small number of samples;  
3. Evidence theory certification by clustering method [48], compared with certification model under clustering feature;  
4. Iris certification algorithm of traditional convolutional neural network [49], compared with the mode of traditional convolutional neural networks trained;  
5. A certification function optimization algorithm based on decision particle swarm optimization algorithm and stable feature [50], compared with certification model with improved certification function;  
6. Iris recognition algorithm based on multi-feature weighted fusion [51], compared with traditional multi-source feature algorithm;  
7. Statistical learning model of convolutional neural networks based on VGG16 model architecture [52];  
8. Faster R-CNN Inception Resnet V2 model architecture [53] for iris certification. The CRR of the three iris libraries compared to the existing algorithms are shown in Table 9.  
As can be seen from Table 9, the algorithm has a strong advantage in the lightweight iris certification process. The deep learning algorithm requires a large amount of data and a sufficiently clear classification label. The amount of data used for learning in this paper is obviously insufficient, so that the final certification effect does not exert the ability of deep learning, which leads to the low certification accuracy. The clustering method can distinguish the differences between the irises by clustering the same category of iris features, which can be proposed for an optimal clustering effect in the current situation. However, in the case of interference and low numbers of training irises, for part of the iris, the feature clustering effect is not obvious, which also makes the algorithm's certification accuracy lower. The decision particle swarm optimization algorithm and the stability feature algorithm are designed for the prerequisites of this paper. By modifying the certification function and improving the environmental inclusiveness of the model, the recognition accuracy is improved. However, this algorithm requires a large amount of iris data for training; for lightweight iris data, the algorithm is insufficiently trained for different iris states, so the recognition accuracy is not as good as the algorithm in this paper. The multi-feature weighted fusion recognition algorithm uses the Haar and LBP algorithm to extract iris features, and the Hamming distance is used for identification; the weight relationship between the two methods is set according to the statistical results of training data. This \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Iris library** & **CASIA-V1** & **CASIA-Iris-Interval** & **CASIA-Iris-Lamp** \\ \hline Deep learning neural network [47] & 76.68\% & 74.69\% & 79.84\% \\ Evidence theory certification by clustering method [48] & 86.45\% & 83.78\% & 81.76\% \\ Traditional convolutional neural network [49] & 83.12\% & 85.46\% & 78.42\% \\ Decision particle swarm optimization algorithm and stable feature [50] & 91.23\% & 90.75\% & 88.73\% \\ Multi-feature weighted fusion [51] & 69.75\% & 70.86\% & 73.42\% \\ VGG16 [52] & 80.74\% & 83.47\% & 84.23\% \\ Faster R-CNN Inception Resnet V2 [53] & 93.42\% & 87.65\% & 94.31\% \\ Algorithm in this paper & 99.78\% & 99.37\% & 99.13\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The CRR of the three iris libraries compared to the existing algorithms. algorithm is a multi-source feature extraction for the ideal state iris library.