## 5 Experimental Results and Discussion
### Results  
Here all the experiments were carried out several times to ensure reliability of the results. The error rates presented in Table 2 are the average error rates calculated for each achievement. As expected, these rates decrease when the resolution of input images increases because high resolutions are able to capture more information.  
Table 2 presents the numerical results obtained on different stages in our method. The network is learned only on \"network-images\" to the results obtained when the network is refined on the entire images. Regarding the transition to learning whole images, we have noted that the results produced by the refined network on the images contain fewer false positives.  
Figure 8: Sample images from the three databases: CASIA-Iris-Thousand (**first row**), UBIRIS.v2 (**second row**) and LG2200 (**third row**).  
The left and right iris images of each subject are supposed to two different classes. At random, we choose 70% data for training of each class and 30% for testing.  
To segment the iris, first we use FCN and then normalize the segmented region into a rectangle of size \(64\times 256\) pixels by using [PERSON]'s rubber sheet method [45]. Then the normalized iris region is fixed into the size \(256\times 256\) and extracted biometric features are employed by using MCNN to define and extract local patterns that fit the iris texture. To increase the recognition accuracy of the proposed method.  
The experimental results show highest accuracy and the best accuracy rates on the three databases with the FCN and MCNN classification. Our approach FCN achieve average segmentation error rate of 0.63%, 0.56% and 0.96% on CASIA-Iris-Thousand, UBIRIS.v2 and LG2200 respectively, and MCNN achieve average feature extraction error rate of 0.71%, 0.85% and 1% respectively, by using Equation (6), which not only exceeds the performance of traditional methods and other previously proposed deep learning-based architectures, but is competitive with the most state-of-art iris recognition algorithms.  
\[Error_{rate}=\frac{1}{N\times m\times n}\sum_{x,y\in(m,n)}G_{(x,y)}\oplus M_{(x,y)} \tag{6}\]  
where \(N,m,n\) are the number of width and length of the images tested, respectively. \(G\) is the ground truth and \(M\) is the generated iris mask, and \(x,y\) are the coordinates of \(G\) and \(M\). The \(\oplus\) represents the XOR operation in equation for evaluate the quarreling pixels between \(G\) and \(M\).  
In addition, we have also compared the results by a multiclass Support Vector Machine(SVM) classifier [52], which have a depth of for kernel output or feature space and linear combination that produces the output with the pre-trained of MCNN without do modified (Figure 9). The training images are used to train the SVM [52]. To make the comparison, we used the same testing and training data. Our proposed MCNN classifier shows lower error than the SVM because the optimal features can be easily extracted with the more convolutional layers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Error Rate (\%)**} \\ \hline
**Method** & **UBIRIS.v2** & **LG2200** & **CASIA-Iris-Thousand** \\ \hline \(CNN_{28}\) & 1.07\% & 1.35\% & 1.21\% \\ \(CNN_{40}\) & 1.02\% & 1.29\% & 1.10\% \\ \(CNN_{56}\) & 0.98\% & 1.17\% & 1.02\% \\ \(CNN_{80}\) & 0.89\% & 1.11\% & 0.93\% \\ SVM & 0.95\% & 1.07\% & 0.79\% \\ MCNN & 0.85\% & 1.00\% & 0.71\% \\ FCN & 0.56\% & 0.96\% & 0.63\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Error Rate evaluation of different architectures used in our Method.  
Figure 9: The framework for iris recognition using SVM Classification.