# Boosting Iris Recognition by Margin-Based Loss Functions
## 1 Introduction  
During the pandemic of COVID-19, non-contact or contactless biometric-based identification methods led the industry in 2020. There is a growing interest in contactless biometric recognition, whether it is using the face [1] and ears [2] or using the iris [3] and voice [4] as the most functional biometric techniques. Contact-based biometrics, such as fingerprints [5], where sensors require that the finger be tightly held, are secure for a person's data, but they are not as secure for their health. Wearing a facial mask is the new norm among people in this pandemic, and applications of face recognition technology [6, 7] have encountered severe problems [8]. A recent work [9] focused on the system for recognizing faces with and without masks. Due to these challenges, iris recognition technology may become more widely adopted since it is contactless and not impacted by masks.  
Based on [PERSON] [10], the four fundamental stages of the iris recognition system are segmentation, normalization, feature extraction, and feature matching. Although accurate iris segmentation is necessary, extracting discriminative features of iris images is the most challenging aspect of these systems. Convolutional neural networks (CNNs) can learn and extract features from highly complex datasets, such as iris images. A variety of solutions can improve CNNs, such as batch normalization, data augmentation, regularization, dropout, and different activation functions. Considering reducing the computational parameters, researchers designed improved structures of the pre-trained network.  
Researchers have paid great attention to optimizing the loss functions to obtain distinctive feature distributions. The SoftMax loss function is one popular loss function for recognition tasks. In terms of inter-class separation, SoftMax loss performs well but is ineffective in minimizing intra-class compactness. For iris recognition systems, where only a few samples are available for each class, the generation of discriminating features is crucial to achieving high classification accuracy. There are differences in the way that SoftMax-based loss functions apply margin penalties. The Angular Softmax Loss (SphereFace) [11] applies a multiplicative angular margin penalty. The Large Margin Cosine Loss (CosFace) [12] adds a cosine margin penalty to the logit directly. Additive Angular Margin Loss (ArcFace) [13] places the additive cosine margin within a geometric angular space.  
Using the MobileNet architecture with the combination of the ArcFace and Triplet loss functions in this paper, we train an iris recognition network taking advantage of both loss functions. We also compare the performance of this improved loss function to three others, consisting of original SoftMax loss, CosFace, and ArcFace. Notably, we try to optimize the network with segmented iris images without normalization. The testing datasets, especially CASIA-Iris-Thousand, have many classes with reflection or eyeglasses that will aid in clarifying the effect of challenging irises on loss functions by analyzing these two iris datasets. This work follows the main contributions:  
* To extract discriminative iris features, our study focuses on implementing state-of-the-art loss functions, especially a combination of ArcFace and Triplet loss with a light-weight MobileNet architecture for the training approach;
* The performance of the proposed system is tested on two public datasets that interfere with the various noises: CASIA-Iris-Thousand [14] and IITD [15] datasets.  
The remaining sections in this paper are arranged as follows: In Section 2, we review a couple of related works with more focus on deep learning-based methods. Section 3 presents an overview of the proposed deep learning approach with described combined loss function. Section 4 demonstrates the proposed system's experimental results. The last section includes conclusions and future directions.