Iris Segmentation Method Research Articles

Exploiting Eye Colors for Better Iris Segmentation in Visible Wavelength Environments

Iris segmentation is a crucial step in iris recognition systems. Iris segmentation in visible wavelength and unconstrained environments is more challenging than ‎segmenting iris images in ideal environments. This paper proposes a new iris segmentation method that exploits the color of human eyes to segment the iris region more accurately. While most of the current iris segmentation methods ignore the color of the iris or deal with ‎grayscale eye images directly, the proposed method takes benefits from iris color to simplify the iris segmentation step. In the first step, we estimate the expected iris center using Haar-like features. The iris color is detected and accordingly, a color-convenient segmentation algorithm is applied to find the iris region. Dealing separately with each iris color set significantly decreases the false segmentation errors and enhances the performance of the iris recognition system. The results of testing the proposed algorithm on the UBIRIS database demonstrate the robustness of our algorithm against different noise factors and non-ideal conditions.

Study the effect of eye diseases on the performance of iris segmentation and recognition using transfer deep learning methods

A new deep learning-based iris recognition system is presented in the current study in the case of eye disease. Current state of art iris segmentation is either based on traditional low accuracy algorithms or heavy-weight deep-based models. In the current study segmentation section, a new iris segmentation method based on illumination correction and a modified circular Hough transform is proposed. The current method also performs a post-processing step to minimize the false positives. Besides, a ground truth of iris images is constructed to evaluate the segmentation accuracy. Many deep learning models (GoogleNet, Inception_ResNet, XceptionNet, EfficientNet, and ResNet50) are applied through the recognition step using the transfer learning approach. In the experiment part, two eye disease-based datasets are used. 684 iris images of individuals with multiple ocular diseases from the Warsaw BioBase V1 and 1,793 iris images from the Warsaw BioBase V2 are also used. The CASIA V3 Interval Iris dataset, which contains 2,639 photographs of healthy iris, is used to train deep models once, and then the transfer learning of this normal-based eye dataset is used to retrain the same deep models using Warsaw BioBase datasets. Different scenarios for training and evaluating participants are used during experiments. The trained models are evaluated using validation accuracy, training time, TPR, FNR, PPR, FDR, and test accuracy. The best accuracies are 98.5% and 97.26%, which are recorded by the ResNet50 (2-layer of transfer learning) model trained on Warsaw BioBase V1 and V2, respectively. Results indicate that the effect of eye diseases is concentrated on the segmentation phase. For recognition, no significant impact is recognized. Some disease that affects the structure (bloody eyes, trauma, iris pigment) can affect the iris recognition step partially. Our study is compared with similar studies in the case of eye diseases. The comparison proves the efficiency and high performance of the proposed methodology against all previous models on the same iris datasets.

Iris Data Compression Based on Hexa-Data Coding

Iris research is focused on developing techniques for identifying and locating relevant biometric features, accurate segmentation and efficient computation while lending themselves to compression methods. Most iris segmentation methods are based on complex modelling of traits and characteristics which, in turn, reduce the effectiveness of the system being used as a real time system. This paper introduces a novel parameterized technique for iris segmentation. The method is based on a number of steps starting from converting grayscale eye image to a bit plane representation, selection of the most significant bit planes followed by a parameterization of the iris location resulting in an accurate segmentation of the iris from the original image. A lossless Hexadata encoding method is then applied to the data, which is based on reducing each set of six data items to a single encoded value. The tested results achieved acceptable saving bytes performance for the 21 iris square images of sizes 256x256 pixels which is about 22.4 KB on average with 0.79 sec decompression average time, with high saving bytes performance for 2 iris non-square images of sizes 640x480/2048x1536 that reached 76KB/2.2 sec, 1630 KB/4.71 sec respectively, Finally, the proposed promising techniques standard lossless JPEG2000 compression techniques with reduction about 1.2 and more in KB saving that implicitly demonstrating the power and efficiency of the suggested lossless biometric techniques.

Lightweight and efficient dual-path fusion network for iris segmentation

In order to tackle limitations of current iris segmentation methods based on deep learning, such as an enormous amount of parameters, intensive computation and excessive storage space, a lightweight and efficient iris segmentation network is proposed in this article. Based on the classical semantic segmentation network U-net, the proposed approach designs a dual-path fusion network model to integrate deep semantic information and rich shallow context information at multiple levels. Our model uses the depth-wise separable convolution for feature extraction and introduces a novel attention mechanism, which strengthens the capability of extracting significant features as well as the segmentation capability of the network. Experiments on four public datasets reveal that the proposed approach can raise the MIoU and F1 scores by 15% and 9% on average compared with traditional methods, respectively, and 1.5% and 2.5% on average compared with the classical semantic segmentation method U-net and other relevant methods. Compared with the U-net, the proposed approach reduces about 80%, 90% and 99% in terms of computation, parameters and storage, respectively, and the average run time up to 0.02 s. Our approach not only exhibits a good performance, but also is simpler in terms of computation, parameters and storage compared with existing classical semantic segmentation methods.

Enhance iris segmentation method for person recognition based on image processing techniques

The limitation of traditional iris recognition systems to process iris images captured in unconstraint environments is a breakthrough. Automatic iris recognition has to face unpredictable variations of iris images in real-world applications. For example, the most challenging problems are related to the severe noise effects that are inherent to these unconstrained iris recognition systems, varying illumination, obstruction of the upper or lower eyelids, the eyelash overlap with the iris region, specular highlights on pupils which come from a spot of light during captured the image, and decentralization of iris image which caused by the person’s gaze. Iris segmentation is one of the most important processes in iris recognition. Due to the different types of noise in the eye image, the segmentation result may be erroneous. To solve this problem, this paper develops an efficient iris segmentation algorithm using image processing techniques. Firstly, the outer boundary segmentation of the iris problem is solved. Then the pupil boundary is detected. Testes are done on the Chinese Academy of Sciences’ Institute of Automation (CASIA) database. Experimental results indicate that the proposed algorithm is efficient and effective in terms of iris segmentation and reduction of time processing. The accuracy results for both datasets (CASIA-V1 and V4) are 100% and 99.16 respectively.

Supervised Contrastive Learning and Intra-Dataset Adversarial Adaptation for Iris Segmentation.

Precise iris segmentation is a very important part of accurate iris recognition. Traditional iris segmentation methods require complex prior knowledge and pre- and post-processing and have limited accuracy under non-ideal conditions. Deep learning approaches outperform traditional methods. However, the limitation of a small number of labeled datasets degrades their performance drastically because of the difficulty in collecting and labeling irises. Furthermore, previous approaches ignore the large distribution gap within the non-ideal iris dataset due to illumination, motion blur, squinting eyes, etc. To address these issues, we propose a three-stage training strategy. Firstly, supervised contrastive pretraining is proposed to increase intra-class compactness and inter-class separability to obtain a good pixel classifier under a limited amount of data. Secondly, the entire network is fine-tuned using cross-entropy loss. Thirdly, an intra-dataset adversarial adaptation is proposed, which reduces the intra-dataset gap in the non-ideal situation by aligning the distribution of the hard and easy samples at the pixel class level. Our experiments show that our method improved the segmentation performance and achieved the following encouraging results: 0.44%, 1.03%, 0.66%, 0.41%, and 0.37% in the Nice1 and 96.66%, 98.72%, 93.21%, 94.28%, and 97.41% in the F1 for UBIRIS.V2, IITD, MICHE-I, CASIA-D, and CASIA-T.

Multi‐source heterogeneous iris segmentation method based on lightweight convolutional neural network

Iris segmentation is a critical step in the iris recognition system. Since the quality of iris database taken under different camera sensors varies greatly, thus most existing iris segmentation methods are designed for a particular collection device. Meanwhile, many iris segmentation methods based on convolutional neural networks (CNNs) require a lot of computational costs and hardware costs (storage space), which are not suitable for deploying on low-performance devices. To address the above problems, an accurate and efficient heterogeneous iris segmentation network is proposed in this paper. First, the authors design an efficient feature extraction network, which combines depth-wise separable convolution with traditional convolution to greatly reduce model parameters and computational cost while maintaining segmentation accuracy. Then, a Multi-scale Context Information Extraction Module (MCIEM) is proposed to extract multi-scale spatial information at a more granular level and enhance the discriminability of the iris region. Finally, a Multi-layer Feature Information Fusion Module (MFIFM) is proposed to reduce the loss of information during the downsampling process. Experimental results on multi-source heterogeneous iris database show that the proposed network can not only achieve state-of-the-art performance but is also more efficient in terms of required parameters, calculated load, and storage space.

Real-time iris segmentation model based on lightweight convolutional neural network

Iris segmentation is an important task in the iris recognition system. Traditional iris segmentation methods are susceptible to irrelevant noise. At present, segmentation methods based on convolutional neural networks (CNNs) have become the main trend in addressing the task. Although these networks achieve promising segmentation performance on the advanced graphics processor, they require a lot of computation and time, which are far from meeting the standards of real-time iris segmentation. To this end, a precise and fast iris segmentation algorithm is proposed. First, an efficient feature extraction network that combines depth-wise separable convolution with dilated convolution is designed to reduce model parameters while maintaining segmentation accuracy. Then, an attention mechanism is introduced to suppress noise interference and enhance the discriminability of the iris region. Finally, an auxiliary training branch is proposed to overcome the vanishing gradient problem. Experimental results show that the proposed method not only achieves state-of-the-art performance but also is more efficient in terms of required parameters, calculated load, and storage space. Specifically, the model parameter is only 0.1 million (M), and the average prediction time is <0.03 seconds (S).

RAG‐Net: ResNet‐50 attention gate network for accurate iris segmentation

Iris segmentation is an important step in the process of iris recognition. Iris images collected under non-cooperative conditions always contain various noise, which is a challenge for iris segmentation. Most U-Net-based methods have made great achievements in iris segmentation. However, this architecture lacks of focusing on target structures of varying shapes, and robustness in segmenting objects with significant shape variations. In this paper, we propose RAG-Net: an efficient iris segmentation method based on deep learning. In contrast to many previous convolutional neural network (CNN)-based iris segmentation methods, we adopted the attention gate (AG) mechanism and ResNet-50 in the U-Net architecture to improve iris segmentation accuracy, the AG module was included in the skip connection part of the RAG-Net architecture to further identify salient feature regions and prune feature responses, which preserve only the activations relevant to the required information, and the ResNet-50 module was used to improve the robustness of the segmentation performance. Using this model, efficient iris segmentation in a non-cooperative environment can be realized. The proposed method was trained and evaluated using the CASIA.v4-distance, CASIA.v4-thousand, UBIRIS.v2, and MICHE-I databases. From the view of the segmentation results, the proposed RAG-Net is one of effective architecture in iris segmentation methods.

Iris segmentation method based on improved UNet++

Iris segmentation method based on improved UNet++

Lightweight iris segmentation network for low-power devices

With the increasing demand for authentication using iris biometrics, iris recognition systems are gradually being deployed on low-power devices. Iris segmentation is a critical step for iris recognition systems. However, existing iris segmentation methods often require a more complex network to obtain higher accuracy, which makes the segmentation model have a greater number of parameters and a higher computation cost. Therefore, these algorithms are not suitable for deployment on low-power devices. To address this problem, we propose a lightweight iris segmentation network, referred to as ATTention Linknet. First, we redesign the feature extraction part of the Linknet, which combines traditional convolution based on an attention mechanism with depthwise separable convolution to reduce computation cost while maintaining accuracy. Then, a proposed loss function that better segments the iris images taken under different cross-sensors is used to train the network. Experimental results on the Chinese Academy of Sciences (CASIA-V4), Indian Institute of Technology Delhi (IITD), and the University of Beira Interior (UBIRIS.V2) iris databases show that the proposed method better balances iris segmentation precision and efficiency compared with state-of-the-art iris segmentation methods. Specifically, our model obtains an error rate of 1.47% on the CASIA-V4 database with 1.69 M parameters.

Iris recognition method based on segmentation

The development of science and studies has led to the creation of many modern means and technologies that focused and directed their interests on enhancing security due to the increased need for high degrees of security and protection for individuals and societies. Hence identification using a person's vital characteristics is an important privacy topic for governments, businesses and individuals. A lot of biometric features such as fingerprint, facial measurements, acid, palm, gait, fingernails and iris have been studied and used among all the biometrics, in particular, the iris gets the attention because it has unique advantages as the iris pattern is unique and does not change over time, providing the required accuracy and stability in verification systems. This feature is impossible to modify without risk. When identifying with the iris of the eye, the discrimination system only needs to compare the data of the characteristics of the iris of the person to be tested to determine the individual's identity, so the iris is extracted only from the images taken. Determining correct iris segmentation methods is the most important stage in the verification system, including determining the limbic boundaries of the iris and pupil, whether there is an effect of eyelids and shadows, and not exaggerating centralization that reduces the effectiveness of the iris recognition system. There are many techniques for subtracting the iris from the captured image. This paper presents the architecture of biometric systems that use iris to distinguish people and a recent survey of iris segmentation methods used in recent research, discusses methods and algorithms used for this purpose, presents datasets and the accuracy of each method, and compares the performance of each method used in previous studies

Heterogeneous iris segmentation method based on modified U-Net

An accurate iris segmentation method is crucial for the iris recognition system. The conventional iris segmentation algorithms have poor adaptability when applied to heterogeneous iris databases. Therefore, researchers have applied deep learning to the field of iris segmentation. A modified U-Net is proposed to perform the iris segmentation for heterogeneous iris databases, referred to as DropBlock and modified shortcut branch U-Net. The main work is as follows: first, EfficientNetV2 based on DropBlock is used as convolutional blocks of U-Net to improve the ability of feature extraction and generalization of the network. Second, an improved shortcut branch structure is proposed for U-Net to reduce the loss of information during the downsampling process. The experimental results on the CASIA-iris-interval-v4, IITD, and UBIRIS.v2 iris databases demonstrate that this method can not only have good versatility but also provide higher accuracy on heterogeneous databases. Compared with some state-of-the-art fusion methods, the proposed method has a significant performance advantage.

A light iris segmentation network

Iris segmentation plays a vital role in the iris recognition system. However, it faces many challenges in non-ideal situations. To improve the iris segmentation performance for possible mobile devices, this paper presents a light iris segmentation method based on fully convolutional network. Firstly, a lightweight fully convolutional iris segmentation network is developed. Secondly, we adopt weighted loss, multi-level feature dense fusion module, multi-supervised training of multi-scale image and generative adversarial network to improve the segmentation performance. The final model is 6.21 M. Experiments show that the proposed method achieves 99.30% PA, 95.35% mIoU on UBIRIS.v2 and 99.66% PA, 96.75% mIoU on CASIA-Iris-Thousand database, which is relatively encouraging for a light iris segmentation network. It takes 41.56 ms and 63.03 ms to segment an image of UBIRIS.v2 and CASIA-Iris-Thousand databases, respectively.

Robust Iris Segmentation Algorithm in Non-Cooperative Environments Using Interleaved Residual U-Net.

Iris segmentation plays an important and significant role in the iris recognition system. The prerequisite for accurate iris recognition is the correctness of iris segmentation. However, the efficiency and robustness of traditional iris segmentation methods are severely challenged in a non-cooperative environment because of unfavorable factors, for instance, occlusion, blur, low resolution, off-axis, motion, and specular reflections. All of the above factors seriously reduce the accuracy of iris segmentation. In this paper, we present a novel iris segmentation algorithm that localizes the outer and inner boundaries of the iris image. We propose a neural network model called “Interleaved Residual U-Net” (IRUNet) for semantic segmentation and iris mask synthesis. The K-means clustering is applied to select saliency points set in order to recover the outer boundary of the iris, whereas the inner border is recovered by selecting another set of saliency points on the inner side of the mask. Experimental results demonstrate that the proposed iris segmentation algorithm can achieve the mean IOU value of 98.9% and 97.7% for inner and outer boundary estimation, respectively, which outperforms the existing approaches on the challenging CASIA-Iris-Thousand database.

Hybrid Iris Segmentation Method Based on CNN and Principal Curvatures

In this article the new hybrid iris image segmentation method based on convolutional neural networks and mathematical methods is proposed. Iris boundaries are found using modified Daugman’s method. Two UNet-based convolutional neural networks are used for iris mask detection. The first one is used to predict the preliminary iris mask including the areas of the pupil, eyelids and some eyelashes. The second neural network is applied to the enlarged image to specify thin ends of eyelashes. Then the principal curvatures method is used to combine the predicted by neural networks masks and to detect eyelashes correctly. The pro- posed segmentation algorithm is tested using images from CASIA IrisV4 Interval database. The results of the proposed method are evaluated by the Intersection over Union, Recall and Precision metrics. The average metrics values are 0.922, 0.957 and 0.962, respectively. The proposed hy- brid iris image segmentation approach demonstrates an improvement in comparison with the methods that use only neural networks.

Iris Segmentation Using Feature Channel Optimization for Noisy Environments

In recent years, iris recognition has been widely used in various fields. As the first step of iris recognition, segmentation accuracy is of great significance to the final recognition. However, iris images exhibit a variety of noise in the real world, which leads to lower segmentation accuracy than the ideal case. To address this problem, this paper proposes an iris segmentation method using feature channel optimization for noisy images. The method for non-ideal environments with noise is more suitable for practical applications. We add dense blocks and dilated convolutional layers to the encoder so that the information gradient flow obtained by different layers can be reused, and the receptive field can be expanded. In the decoder, based on Jensen-Shannon (JS) divergence, we first recalculate the weight of the feature channels obtained from each layer, which enhances the useful information and suppresses the interference information in the noisy environments to boost the segmentation accuracy. The proposed architecture is validated in the CASIA v4.0 interval (CASIA) and IIT Delhi v1.0 datasets (IITD). For CASIA, the mean error rate is 0.78%, and the F-measure value is 98.21%. For IITD, the mean error rate is 0.97%, and the F-measure value is 97.87%. Experimental results show that the proposed method outperforms other state-of-art methods under noisy environments, such as Gaussian blur, Gaussian noise, and salt and pepper noise.

Iris Recognition Method for Non Ideal Images

The issue of counterfeiting is one of the most pressing threats to people’s security because of the many complex security problems. Many systems used in the field of verification and identification but the basic criterion for choosing is the degree of safety identification. Identification and verification systems use biometric characteristics of individuals. The system of iris recognition is one of the most important biometric recognition systems. The system used iris images for 100 persons belonging to the UBIRIS V1 database. Each person has 5 captures; the total number is 500 images. A hybrid iris segmentation method combined two algorithms ChanVese (CV) and grabcut to obtain high verification rate and to detect the outer boundary and localize the iris region. The system includes several steps starting with acquiring the image of the iris, enhancing the image quality, segment iris region, determining the coordinates of iris center and radius of the pupil, convert the iris coordinates of the cutting segments from Cartesian coordinates to the polar coordinates to reduce the processing time and to avoid the noise image problems due to eyelids and eyelashes, then utilize Gabor wavelets to extract the features of iris, and finally perform the matching process using Euclidian distance. The performance indicators, which include the recognition rate, false rejection rate(FRR), False Acceptance Rate(FAR), Correct identification rate(CIR), are as follows(97.5%, 39%, 0.025%, 0.0975). The proposed system has many advantages includes high processing capability of low-quality images and low storage capacity.

Towards Complete and Accurate Iris Segmentation Using Deep Multi-Task Attention Network for Non-Cooperative Iris Recognition

Iris images captured in non-cooperative environments often suffer from adverse noise, which challenges many existing iris segmentation methods. To address this problem, this paper proposes a high-efficiency deep learning based iris segmentation approach, named IrisParseNet . Different from many previous CNN-based iris segmentation methods, which only focus on predicting accurate iris masks by following popular semantic segmentation frameworks, the proposed approach is a complete iris segmentation solution, i.e., iris mask and parameterized inner and outer iris boundaries are jointly achieved by actively modeling them into a unified multi-task network. Moreover, an elaborately designed attention module is incorporated into it to improve the segmentation performance. To train and evaluate the proposed approach, we manually label three representative and challenging iris databases, i.e., CASIA.v4-distance, UBIRIS.v2, and MICHE-I, which involve multiple illumination (NIR, VIS) and imaging sensors (long-range and mobile iris cameras), along with various types of noises. Additionally, several unified evaluation protocols are built for fair comparisons. Extensive experiments are conducted on these newly annotated databases, and results show that the proposed approach achieves state-of-the-art performance on various benchmarks. Further, as a general drop-in replacement, the proposed iris segmentation method can be used for any iris recognition methodology, and would significantly improve the performance of non-cooperative iris recognition.

PixISegNet: pixel‐level iris segmentation network using convolutional encoder–decoder with stacked hourglass bottleneck

In this paper, we present a new iris ROI segmentation algorithm using a deep convolutional neural network (NN) to achieve the state-of-the-art segmentation performance on well-known iris image data sets. The authors' model surpasses the performance of state-of-the-art Iris DenseNet framework by applying several strategies, including multi-scale/ multi-orientation training, model training from scratch, and proper hyper-parameterisation of crucial parameters. The proposed Pix I SegNet consists of an autoencoder which primarily uses long and short skip connections and a stacked hourglass network between encoder and decoder. There is a continuous scale up-down in stacked hourglass networks, which helps in extracting features at multiple scales and robustly segments the iris even in an occluded environment. Furthermore, cross-entropy loss and content loss optimise the proposed model. The content loss considers the high-level features, thus operating at a different scale of abstraction, which compliments the cross-entropy loss, which considers pixel-to-pixel classification loss. Additionally, they have checked the robustness of the proposed network by rotating images to certain degrees with a change in the aspect ratio along with blurring and a change in contrast. Experimental results on the various iris characteristics demonstrate the superiority of the proposed method over state-of-the-art iris segmentation methods considered in this study. In order to demonstrate the network generalisation, they deploy a very stringent TOTA (i.e. train-once-test-all) strategy. Their proposed method achieves E 1 scores of 0.00672, 0.00916 and 0.00117 on UBIRIS-V2, IIT-D and CASIA V3.0 Interval data sets, respectively. Moreover, such a deep convolutional NN for segmentation when included in an end-to-end iris recognition system with a siamese based matching network will augment the performance of the siamese network.