# Periocular Recognition in the Wild: Implementation of RGB-OCLBCP Dual-Stream CNN
## 5 Experiments
### Experimental Results
#### 5.2.2 Performance Evaluation on Recognition and Verification Tasks
Evaluation on CASIA-Iris Distance Database  
To evaluate whether our approach performs well on another standard database, we also tested its performance in a more subjective experiment with CASIA-iris distance database. This database consists of 142 subjects under a long-range subject-camera distance and indoor environment. The images were captured by a high-resolution camera so both dual-eye iris and periocular are included in the image region of interest. The further details of the database can be found in [29]. The experimental protocol for recognition was designed with the ratio of the gallery set to probe set as 50:50 and the division process was repeated three times. The experimental protocol for the verification was designed by randomly selecting 250 reference-query pairs as \"same\" and another 250 pairs as \"not same\". This selection process was repeated three times. According to Table 3, the proposed network achieved the highest average accuracies for Rank-1 and Rank-5 recognitions with 96.62 \(\pm\) 1.3% and 98.45 \(\pm\) 0.4%, respectively. Besides, FaceNet achieved the second-best performance with 96.09 \(\pm\) 2.1% and 98.10 \(\pm\) 0.4% for Rank-1 and Rank-5 recognition accuracies, respectively. We also present in Figure 5(a) the Rank-1 to Rank-10 recognition results. As can be seen, our network achieved the best results among the benchmark networks. For the verification, the proposed network achieved the lowest EER accuracy as 4.35 \(\pm\) 0.5% and AUC as 0.9860. Interestingly, DeepIrisNet-B attained second lowest performance with 5.87 \(\pm\) 1.5% for EER and 0.9756 as AUC. Figure 5(b) illustrates the ROC curve, which demonstrates that our network obtained the best performance of AUC and the lowest EER. Both recognition and verification results indicate that the proposed network is capable of learning the features of the RGB image and OCLBCP decently for improving the performance of recognition and verification tasks. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Networks**} & \multicolumn{2}{c}{**AR**} & \multicolumn{2}{c}{**CASIA-Iris**} & \multicolumn{2}{c}{**UBIPr**} & \multicolumn{2}{c}{**Ethnic-Ocular**} \\ \cline{2-9}  & _EER (\%)_ & _AUC_ & _EER (\%)_ & _AUC_ & _EER (\%)_ & _AUC_ & _EER (\%)_ & _AUC_ \\ \hline AlexNet & 14.53 & 0.9363 & 8.06 \(\pm\) 5.3 & 0.9533 & 7.11 \(\pm\) 2.9 & 0.9805 & 16.47 \(\pm\) 1.6 & 0.9139 \\ DeepIrisNet-A & 7.69 & 0.9751 & 7.51 \(\pm\) 1.1 & 0.9674 & 5.07 \(\pm\) 2.2 & 0.9877 & 8.79 \(\pm\) 1.7 & 0.9689 \\ DeepIrisNet-B & 8.12 & 0.9741 & 5.87 \(\pm\) 1.5 & 0.9756 & 4.29 \(\pm\) 0.9 & 0.9890 & 8.77 \(\pm\) 1.1 & 0.9693 \\ FaceNet & 9.40 & 0.9692 & 6.10 \(\pm\) 2.2 & 0.9738 & 5.46 \(\pm\) 1.5 & 0.9870 & 11.67 \(\pm\) 1.2 & 0.9489 \\ LCNN29 & 9.39 & 0.9737 & 6.34 \(\pm\) 1.6 & 0.9719 & 6.34 \(\pm\) 2.1 & 0.9849 & 10.95 \(\pm\) 1.6 & 0.9536 \\ Multi-fusion CNN & 7.69 & 0.9756 & 8.69 \(\pm\) 1.1 & 0.9594 & 4.09 \(\pm\) 2.1 & 0.9913 & 8.63 \(\pm\) 1.3 & 0.9681 \\ VGG16 & 7.69 & 0.9747 & 7.42 \(\pm\) 1.7 & 0.9681 & 4.38 \(\pm\) 1.3 & 0.9892 & 9.43 \(\pm\) 2.5 & 0.9553 \\
**Proposed Network** & **5.13** & **0.9882** & **4.35 \(\pm\) 0.5** & **0.9860** & **3.41 \(\pm\) 1.8** & **0.9938** & **6.63 \(\pm\) 1.5** & **0.9818** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation of the verification task on the AR database, CASIA-iris distance database, UBIPr database, and Ethnic-ocular database. The highest accuracy is written in bold. Figure 5: Performances of recognition and verification tasks on AR database. The figures are best viewed in colour. Evaluation on UBIPr Database  
We also conducted another more challenge experiment with the UBIPr database to verify the robustness of the proposed network. This database consists of 342 subjects with varying subject-camera distances, levels of illumination, and poses [12]. This experiment evaluated the performance of all the networks with varying poses and subject-camera distances.