# 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  
We used Ethnic-ocular, as well as three public databases, the AR [51], CASIA-iris distance [29], and UBIPr [12], to evaluate the performances of the proposed network and other benchmark networks. All the experimental results are outlined in the following sections. Evaluation on AR Database  
The AR database is designed under a constrained environment, which consists of 117 subjects with varying neutrals, expressions, illuminations, and occlusion conditions, who were captured across two sessions. We opted for this database as it provides a good baseline to evaluate the robustness and performance in constrained environments, such as different levels of illuminations and expressions in an indoor environment. Extraction for the periocular regions was done by using the method in [45]. The experimental protocol for recognition was as follows: ten images for each subject were used as gallery sets from Session 1 and another ten per subject as probe sets from Session 2. On the other hand, the verification protocol was designed by randomly selecting 250 reference-query pairs as \"same' and another 250 pairs as \"not same\". Table 3 presents the performance comparisons on recognition. As can be seen in the table, our network achieved the highest Rank-1 and Rank-5 recognition accuracies with 96.32% and 98.80%, respectively. Likewise, DeepIrisNet-A had the best performance on Rank-1 and Rank-5 among the other benchmark approaches, which only achieved accuracies of 95.24% and 98.38%, respectively. Figure 4(a) illustrates that the proposed network outperformed other approaches with respect to all the benchmarks from Rank-1 to Rank-10 recognition. For the verification task, we report the experimental results in Table 4. The proposed network also achieved the best EER and AUC with 5.13% and 0.9880, respectively. DeepIrisNet-A, Multi-fusion CNN, and VGG16 achieved the second-best performances among the other benchmark approaches with 7.69% for EER. Figure 4(b) illustrates the ROC curve and shows that the proposed network (red solid line with diamond) outperformed the benchmark approaches. \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}  & _Rank-1_ & _Rank-5_ & _Rank-1_ & _Rank-5_ & _Rank-1_ & _Rank-5_ & _Rank-1_ & _Rank-5_ \\ \hline AlexNet & 93.59 & 96.75 & 95.00 \(\pm\) 1.8 & 96.98 \(\pm\) 2.5 & 84.88 \(\pm\) 2.5 & 96.01 \(\pm\) 1.8 & 64.72 \(\pm\) 3.3 & 82.98 \(\pm\) 2.5 \\ DeepIrisNet-A & 95.24 & 98.38 & 95.95 \(\pm\) 2.1 & 98.15 \(\pm\) 0.6 & 90.30 \(\pm\) 1.2 & 97.41 \(\pm\) 1.1 & 79.54 \(\pm\) 3.1 & 90.43 \(\pm\) 2.4 \\ DeepIrisNet-B & 94.44 & 97.18 & 95.79 \(\pm\) 2.6 & 97.75 \(\pm\) 0.6 & 90.20 \(\pm\) 1.7 & 97.43 \(\pm\) 0.5 & 81.13 \(\pm\) 3.1 & 92.37 \(\pm\) 1.2 \\ FaceNet & 94.19 & 97.75 & 96.09 \(\pm\) 2.1 & 98.10 \(\pm\) 0.4 & 90.24 \(\pm\) 1.4 & 97.36 \(\pm\) 0.4 & 78.71 \(\pm\) 3.7 & 92.19 \(\pm\) 1.6 \\ LCNN29 & 94.27 & 97.52 & 96.01 \(\pm\) 2.0 & 97.85 \(\pm\) 0.9 & 90.28 \(\pm\) 1.7 & 97.18 \(\pm\) 0.7 & 79.35 \(\pm\) 2.6 & 92.17 \(\pm\) 1.8 \\ Multi-fusion CNN & 96.07 & 98.71 & 95.81 \(\pm\) 1.9 & 97.67 \(\pm\) 1.0 & 90.75 \(\pm\) 1.0 & 97.44 \(\pm\) 0.3 & 81.79 \(\pm\) 3.5 & 93.03 \(\pm\) 1.3 \\ VGG16 & 94.20 & 97.61 & 95.88 \(\pm\) 0.1 & 97.99 \(\pm\) 0.5 & 90.24 \(\pm\) 1.4 & 90.99 \(\pm\) 1.1 & 76.43 \(\pm\) 2.2 & 91.29 \(\pm\) 1.5 \\
**Proposed Network** & **96.32** & **98.80** & **96.62 \(\pm\) 1.3** & **98.45 \(\pm\) 0.4** & **91.28 \(\pm\) 1.2** & **98.59 \(\pm\) 0.4** & **85.03 \(\pm\) 1.9** & **94.23 \(\pm\) 1.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation of the recognition task on the AR database, CASIA-iris distance database, UBIPr database, and Ethnic-ocular database. The highest accuracy is written in bold.