Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Iris Recognition <s> In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision. <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Iris Recognition <s> Iris is one of the popular biometrics that is widely used for identity authentication. Different features have been used to perform iris recognition in the past. Most of them are based on hand-crafted features designed by biometrics experts. Due to tremendous success of deep learning in computer vision problems, there has been a lot of interest in applying features learned by convolutional neural networks on general image recognition to other tasks such as segmentation, face recognition, and object detection. In this paper, we have investigated the application of deep features extracted from VGG-Net for iris recognition. The proposed scheme has been tested on two well-known iris databases, and has shown promising results with the best accuracy rate of 99.4%, which outperforms the previous best result. <s> BIB002 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Iris Recognition <s> Despite significant advances in iris recognition (IR), the efficient and robust IR at scale and in non-ideal conditions presents serious performance issues and is still ongoing research topic. Deep Convolution Neural Networks (DCNN) are powerful visual models that have reported state-of-the-art performance in several domains. In this paper, we propose deep learning based method termed as DeepIrisNet for iris representation. The proposed approach bases on very deep architecture and various tricks from recent successful CNNs. Experimental analysis reveal that proposed DeepIrisNet can model the micro-structures of iris very effectively and provides robust, discriminative, compact, and very easy-to-implement iris representation that obtains state-of-the-art accuracy. Furthermore, we evaluate our iris representation for cross-sensor IR. The experimental results demonstrate that DeepIrisNet models obtain a significant improvement in cross-sensor recognition accuracy too. <s> BIB003 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Iris Recognition <s> Iris recognition has proved to be one of the most reliable and stable biometric for human identification. This paper outlines an iris recognition approach based on deep learning. In addition, contour based feature vector has been used to discriminate samples belonging to different classes i.e. difference of sclera-iris and iris-pupil contours, and is named as “Unique Signature”. Moreover, contours of both the boundaries are extracted using the radius vector function. The process of feature extraction starts with preprocessing i.e. removal of specular highlight, then weighted centroid of the eye is used as the reference to extract the contour points. Further, the aforementioned approach is rotation, translation and scale invariant. Also, deep belief network (DBN) with modified back-propagation algorithm based feed-forward neural network (RVLR-NN) has been used for classification. Simulation results have been observed for the benchmark CASIA iris database i.e. CASIA-Iris-V4. Further, classification accuracies have been observed at different levels of signal-to-noise ratio (SNR) i.e. additive white Gaussian noise (AWGN). Also, comparisons have been made with other techniques using contour based features for iris detection. Further, all simulations have been performed in MATLAB. The proposed approach outperforms both the neural network based single and dual boundary approaches in terms of classification accuracies. Observed results have shown good classification accuracies especially at high noise levels. <s> BIB004 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Iris Recognition <s> This paper proposes an accurate and generalizable deep learning framework for iris recognition. The proposed framework is based on a fully convolutional network (FCN), which generates spatially corresponding iris feature descriptors. A specially designed Extended Triplet Loss (ETL) function is introduced to incorporate the bit-shifting and non-iris masking, which are found necessary for learning discriminative spatial iris features. We also developed a sub-network to provide appropriate information for identifying meaningful iris regions, which serves as essential input for the newly developed ETL. Thorough experiments on four publicly available databases suggest that the proposed framework consistently outperforms several classic and state-of-the-art iris recognition approaches. More importantly, our model exhibits superior generalization capability as, unlike popular methods in the literature, it does not essentially require database-specific parameter tuning, which is another key advantage over other approaches. <s> BIB005 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Iris Recognition <s> Iris biometrics-based recognition and verification systems, is an important and well-studied problem. However, recent advances in deep convolution networks make them a viable tool for extracting meaningful attributes and the process of using such an algorithmically generated feature set to classify images may be applied to the iris recognition method. This paper explores the applicability of the Convolutional Neural Network to iris biometrics. It explores the benefits of automatically-generated features compared to the traditional method of hand-crafted features and uses algorithms based on fine tuned models of deep residual networks, to solve both the recognition and the verification problems, resulting in a 99.8% recognition rate. <s> BIB006 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Iris Recognition <s> Generating iris images which look realistic is both an interesting and challenging problem. Most of the classical statistical models are not powerful enough to capture the complicated texture representation in iris images, and therefore fail to generate iris images which look realistic. In this work, we present a machine learning framework based on generative adversarial network (GAN), which is able to generate iris images sampled from a prior distribution (learned from a set of training images). We apply this framework to two popular iris databases, and generate images which look very realistic, and similar to the image distribution in those databases. Through experimental results, we show that the generated iris images have a good diversity, and are able to capture different part of the prior distribution. <s> BIB007 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Iris Recognition <s> Abstract CNN-based iris segmentations have been proven to be superior to traditional iris segmentation techniques in terms of segmentation error metrics. To properly utilize them in a traditional biometric recognition systems requires a parameterization of the iris, based on the generated segmentation, to obtain the normalised iris texture typically used for feature extraction. This is an unsolved problem. We will introduce a method to parameterize CNN based segmentation, bridging the gap between CNN based segmentation and the rubbersheet-transform. The parameterization enables the CNN segmentation as full segmentation step in any regular iris biometric system, or alternatively the segmentation can be utilized as a noise mask for other segmentation methods. Both of these options will be evaluated. <s> BIB008 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Iris Recognition <s> Most iris recognition pipelines involve three stages: segmenting into iris/non-iris pixels, normalization the iris region to a fixed area, and extracting relevant features for comparison. Given recent advances in deep learning it is prudent to ask which stages are required for accurate iris recognition. Lojez et al. (IWBF 2019) recently concluded that the segmentation stage is still crucial for good accuracy.We ask if normalization is beneficial? Towards answering this question, we develop a new iris recognition system called ThirdEye based on triplet convolutional neural networks (Schroff et al., ICCV 2015). ThirdEye directly uses segmented images without normalization. We observe equal error rates of 1.32%, 9.20%, and 0.59% on the ND-0405, UbirisV2, and IITD datasets respectively. For IITD, the most constrained dataset, this improves on the best prior work. However, for ND-0405 and UbirisV2,our equal error rate is slightly worse than prior systems. Our concluding hypothesis is that normalization is more important for less constrained environments. <s> BIB009 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Iris Recognition <s> Iris recognition has been an active research area during last few decades, because of its wide applications in security, from airports to homeland security border control. Different features and algorithms have been proposed for iris recognition in the past. In this paper, we propose an end-to-end deep learning framework for iris recognition based on residual convolutional neural network (CNN), which can jointly learn the feature representation and perform recognition. We train our model on a well-known iris recognition dataset using only a few training images from each class, and show promising results and improvements over previous approaches. We also present a visualization technique which is able to detect the important areas in iris images which can mostly impact the recognition results. We believe this framework can be widely used for other biometrics recognition tasks, helping to have a more scalable and accurate systems. <s> BIB010 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Iris Recognition <s> Presently, lots of previous studies on biometrics employ convolutional neural networks (CNN) which requires a large amount of labeled training data. However, biometric data are considered as important personal information, and it is difficult to obtain large amounts of data due to individual privacy issues. Training with a small amount of data is a major cause of overfitting and low testing accuracy. To resolve this problem, previous studies have performed data augmentation that are based on geometric transforms and the adjustment of image brightness. Nevertheless, the data created by these methods have high correlation with the original data, and they cannot adequately reflect individual diversities. To resolve this problem, this study proposes iris image augmentation based on a conditional generative adversarial network (cGAN), as well as a method for improving recognition performance that uses this augmentation method. In our method, normalized iris images that are generated through arbitrary changes in the iris and pupil coordinates are used as input in the cGAN-based model to generate iris images. Due to the limitations of the cGAN model, data augmentation, which uses the periocular region, was found to fail with regard to the improvement of performance. Based on this information, only the iris region was used as input for the cGAN model. The augmentation method proposed in this paper was tested using NICE.II training dataset (selected from UBIRS.v2), MICHE database, and CASIA-Iris-Distance database. The results showed that the recognition performance was improved compared to existing studies. <s> BIB011
Compared to face recognition, deep learning models made their ways to iris recognition with a few years delay. As one of the first works using deep learning for iris recognition, in BIB002 Minaee et al. showed that features extracted from a pre-trained CNN model trained on Im-ageNet are able to achieve a reasonably high accuracy rate for iris recognition. In this work, they used features derived from different layers of VGGNet BIB001 , and trained a multi-class SVM on top of it, and showed that the trained model can achieve state-of-the-art accuracy on two iris recognition benchmarks, CASIA-1000 and IIT Delhi databases. They also showed that features extracted from the mid-layers of VGGNet achieve slightly higher accuracy from the the very last layers. In another work BIB003 , Gangwar and Joshi proposed an iris recognition network based on convolutional neural network, which provides robust, discriminative, compact resulting in very high accuracy rate, and can work pretty well in cross-sensor recognition of iris images. In BIB004 , Baqar and colleagues proposed an iris recognition framework based on deep belief networks, as well as contour information of iris images. Contour based feature vector has been used to discriminate samples belonging to different classes i.e., difference of sclerairis and iris-pupil contours, and is named as Unique Signature. Once the features extracted, deep belief network (DBN) with modified back-propagation algorithm based feed-forward neural network (RVLR-NN) has been used for classification. In BIB005 , Zhao and Kumar proposed an iris recognition model based on "Deeply Learned Spatially Corresponding Features". The proposed framework is based on a fully convolutional network (FCN), which outputs spatially corresponding iris feature descriptors. They also introduce a specially designed "Extended Triplet Loss (ETL)" function to incorporate the bit-shifting and non-iris masking. The triplet network is illustrated in Figure 16 . They also developed a sub-network to provide appropriate information for identifying meaningful iris regions, which serves as essential input for the newly developed ETL. They were able to outperform several classic and state-of-the-art iris recognition approaches on a few iris databases. Fig. 16 The block-diagram of triplet network used for iris recognition, courtesy of BIB005 . In another work  , Alaslani et al. developed an iris recognition system, based on deep features extracted from AlexNet, followed by a multi-class classification, and achieved high accuracy rates on CASIA-Iris-V1, CASIA-Iris-1000 and, CASIA-Iris-V3 Interval databases. In BIB006 , Menon shows the applications of convolutional features from a fine-tuned pre-trained model for both identification and verification problems. In BIB008 , Hofbauer and colleagues proposed a CNN based algorithm for segmentation of iris images, which can results in higher accuracies than previous models. In another work BIB009 , Ahmad and Fuller developed an iris recognition model based on triplet network, call ThirdEye. Their work directly uses the segmented, un-normalized iris images, and is shown to achieve equal error rates of 1.32%, 9.20%, and 0.59% on the ND-0405, UbirisV2, and IITD datasets respectively. In a more recent work BIB010 , Minaee and colleagues proposed an algorithm for iris recognition based using a deep transfer learning approach. They trained a CNN model (by fine-tuning a pre-trained ResNet model) on an iris dataset, and achieved very accurate recognition on the test set. With the rise of deep generative models, there have been works that apply them to iris recognition. In BIB007 , Minaee et al proposed an algorithm for iris image generation based on convolutional GAN, which can generate realistic iris images. These images can be used for augmenting the training set, resulting in better feature representation and higher accuracy. Four sample iris images generated by this work (over different training epochs) are shown in Figure 17 . In BIB011 , Lee and colleagues proposed a data augmentation technique based on GAN to augment the training data for iris recognition, resulting in a higher accuracy rate. They claim that historical data augmentation techniques such as geometric transformations and brightness adjustment result in samples with very high correlation with the original ones, but using augmentation based on a conditional generative adversarial network can result in higher test accuracy.