# Boosting Iris Recognition by Margin-Based Loss Functions
## 5 Conclusions and Future Work  
Through the present article, the researchers proposed a deep learning framework for iris recognition based on MobileNet architecture by using a combined loss function with ArcFace and Triplet. ArcFace has a larger margin on semi-hard instances and keeps a small margin on easy ones; and increases the margins between classes, Triplet Loss narrows margins within a single class This somehow makes ArcFace work better on iris recognition tasks. As expected, the robust discriminative proposed CNN architecture using the combined loss function achieved significant performance with the EER = 0.45% for IITD and EER = 1.87% for CASIA-Iris-Thousand without normalization step. The obtained results, especially for CASIA-Iris-Thousand, have proved that the combined loss function can effectively be applied to large-scale iris identities. According to the above finding, it can be realized that the results of the Arcface studied in the present investigation lead to better use of existing state-of-the-art loss functions for iris recognition systems.  
Various limitations, such as noisy data, differences and similarities between classes, have led to the development of multimodal biometrics, which utilizes multiple sources of information to authenticate the person. Using a fusion of matching and decision score level method, the proposed approach could be applied in a multimodal context. As shown in [PERSON] et al. (2015), fusing iris and retina or periocular modalities represents a robust combination. For future developments of the proposed approach, we will focus on multimodal biometrics. In future research, we will apply the proposed method to various datasets collected in an unconstrained environment with more recent deep learning architecture. In addition, we will use other loss functions independent of determining the margins and scale.  
Conceptualization, R.A.L.; Data curation, S.D.; Formal analysis, R.A.L.; Methodology, H.H. and R.A.L.; Resources, H.H. and R.A.L.; Validation, S.D., R.A.L. and H.H.; Visualization, [PERSON]; Writing--original draft, R.A.L. and S.D.; Writing--review & editing, S.D., [PERSON] and R.A.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The analysed datasets are publicly available. Related references are reported in the References section.  
The authors would like to thank [PERSON] for providing the implementation of Keras_insightface, which is available on Github, accessed on April 2021 ([[https://github.com/leondgarse/Keras_insightface/](https://github.com/leondgarse/Keras_insightface/)]([https://github.com/leondgarse/Keras_insightface/](https://github.com/leondgarse/Keras_insightface/)) access on 25 April 2021).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & & **IITD** & \multicolumn{4}{c}{**CASIA-Iris-Thousand**} \\ \cline{2-7}  & **TPR (\%)** & **EER (\%)** & **AUC (\%)** & **TPR (\%)** & **EER (\%)** & **AUC (\%)** \\ \hline DeepIrisNet ([PERSON] et al., 2018) & 95.03 & 1.17 & 99.02 & 86.86 & 4.01 & 97.93 \\ UniNet ([PERSON] et al., 2018) & 97.35 & 0.89 & 99.20 & 90.61 & 2.79 & 98.67 \\ CapsuleNet ([PERSON] et al., 2018) & 97.39 & 0.76 & 99.27 & 91.04 & 3.05 & 98.60 \\ T-Center ([PERSON] et al., 2018) & 97.43 & 0.74 & 99.30 & 92.54 & 2.36 & 98.75 \\
**Proposed method (MobileNet + (ArcFace + Triplet))** & **100** & **0.45** & **99.99** & **99.66** & **1.87** & **99.84** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the performance on two public datasets.