# Boosting Iris Recognition by Margin-Based Loss Functions
## 4 Experiments and Results
### Evaluation  
To generate a stable evaluation result and reduce the calculating complexity, we first extract the embedding value from all images, and then sum up and normalize all embedding vectors in the same class as the registered base embedding vector. We use this vector as an embedding vector for this class and measure distance by calculating cosine distances between all embedding vectors and all registered base embedding vectors. Evaluation results consist of two parts: distances among all images and between all vectors  
Figure 3: Depthwise Separable Convolutional block used in MobileNet. registered, a label that shows whether each score is positively or negatively classified. The final evaluating results contain two parts score for distances between all images and all registered vectors, and a label showing whether each score should be classified positive or negative. We compare the performance of selected loss functions using ROC curves and the EER to quantify the verification process. In addition to ROC curves for each dataset, we also select TPR at FPR = 0.1% and calculate the AUC to provide other feedback. Figures 4 and 5 show the ROC curves obtained on IITD and CASIA-Iris-Thousand datasets, respectively. Tables 2 and 3 show the results based on TPR, EER, and AUC. The proposed CNN model that uses ArcFace + Triplet loss is more effective for recognizing iris patterns. To make our conclusion clearer, we plot the ROC curve for combined loss separately in Figures 6 and 7. Implementing ArcFace + Triplet loss achieves the lowest EER compared with the Arcface, CosFace, and SoftMax loss. The performance drops to the lower amount of AUC and TPR when evaluating the algorithm with Arcface and CosFace. Additionally, we plot the ROC of the SoftMax loss function to compare two margin-based loss functions with the SoftMax loss function. CASIA-Iris-Thousand is a more complex dataset than IITD because it contains images with eyeglasses and specular reflections. Therefore, the EER for CASIA-Iris-Thousand is higher than EER for IITD. On the evaluated results, we also plot the histograms of positive and negative distributions. This histogram visually compares how the proposed method with the margin-based loss functions can distinguish iris feature vectors. We can see a clear decision margin distinguished in Figure 8, where the positive similarities for ArcFace are more gathered to the right of the figure, and the overlapping area is smaller. Though Figure 9 is not as clear as Figure 8, we also see a more narrow distribution for CosFace and ArcFace positive similarities. As illustrated in Figures 8 and 9, ArcFace + Triplet is the best among all of them because there is a smaller overlap area between the red and blue distributions, and its positive distribution is more tended to the right than the other one. SoftMax loss is worst at separation. The margin between red and blue is smaller, and the overlapped area is broader. However, its negative red distribution is narrower and more concentrated, which means a smaller variance value. CosFace works similar to ArcFace, only adding a different margin between negative and positive. Thus, their results are identical, as only ArcFace + Triplet has a smaller overlapped area, making it a better separator. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Implementation** & **EER (\%)** & **TPR (\%)** & **AUC (\%)** \\ \hline MobileNet + Softmax & \(1.11\) & 99.77 & 99.92 \\ MobileNet + Cosface & 0.78 & 100 & **99.98** \\ MobileNet + ArcFace & 0.45 & 100 & 99.98 \\
**MobileNet + (ArcFace + Triplet)** & **0.45** & **100** & **99.99** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Testing results of different implementation on IIT Delhi. Figure 6: ROC curve of ArcFace + Triplet loss on IITD. We compare our model with three deep learning models, including DeepIrisNet [30], UniNet [35], CapsuleNet [34] and T-Center [39]. Table 4 shows that the proposed method with ArcFace + Triplet achieves the lowest value of EER for both datasets compared with the original ArcFace + Triplet.