# Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration
## 3 Lightweight Unsteady State Constrained Iris Certification
### Decision Layer  
The image processing layer can extract the original stable map points for defocused iris conditions; the algorithms in the feature recognition layer are designed to eliminate the effects of iris deflection as much as possible. However, the accuracy of recognition is not only affected by the image itself, but also has a great correlation with device parameters, collector proficiency and external illumination environment. Therefore, the same algorithm may produce different recognition results in different external environments. In the case of ensuring the same collection equipment and only consider the effects of illumination, in the decision layer, two algorithms of feature recognition layer are tested and analyzed the experimental results to judge the credibility of the two algorithms' recognition results. The certification conclusion can be obtained through maximum credibility.  
In the output of feature recognition layer, the results offer two outcomes: first, the test iris belongs to a specific category in the template iris library; second, the test iris does not belong to any category in the template iris library. Based on these two cases, the credibility of each recognition algorithm in the feature recognition layer is calculated separately under certain illumination conditions.  
1. The test iris belongs to a specific category in the template iris library  
Select \(m\) test irises, denoted as \(T1_{i}\). Each test iris corresponds to a template iris of the same category, consisting of \(m\) template irises, denoted as \(M1_{i}\). Comparing each test iris with all template irises, the sum of the cases in the recognition algorithm where the same category of iris is identified and different categories are excluded is calculated. The algorithm credibility is the ratio of the sum value and the total number of comparisons. The algorithm credibility \(w_{i}\) is calculated by Equation (8).  
\[w_{i}=\frac{\left(1+\sum\limits_{i=1}^{m}Sgn(F(T_{i},M_{i}))\right)/2+\left(1+ \sum\limits_{y=1}^{m,y*i}\sum\limits_{i=1}^{m}Sgn(F(T_{i},M_{y}))\right)/2}{m \times m}, \tag{8}\]  
where \(F_{1}(T1_{i},M1_{i})\) is the result of the same category comparison.  
2. The test iris does not belong to any category in the template iris library  
Select \(t\) test irises, denoted as \(T2_{i}\). Different types of irises with test iris make up \(r\) template iris, denoted as \(M2_{i}\). Comparing each test iris with all template irises, the sum of cases where the different categories are excluded in the recognition algorithm is calculated. Then, Equation (9) is used to calculate the algorithm credibility \(k_{i}\).  
\[k_{i}=\frac{\left(1+\sum\limits_{y=1}^{r}\sum\limits_{i=1}^{t}Sgn(F(T2_{i},M2 _{y}))/2\right)}{t\times r}, \tag{9}\]  
where \(F_{2}(T2_{i},M2_{y})\) is the algorithm comparison result between the test iris and the template iris.  
When making the decision vote, the result with the most credibility is selected as the certification conclusion based on the credibility of each recognition algorithm under specific illumination conditions. The flexibility of the algorithm's influence on illumination and the correct recognition rate of the algorithm can be effectively improved by using this method.