Iris Segmentation Research Articles

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.

IrisSeg-drunk: enhanced iris segmentation and classification of drunk individuals using Modified Circle Hough Transform

IrisSeg-drunk: enhanced iris segmentation and classification of drunk individuals using Modified Circle Hough Transform

Advancements in Iris Recognition: WAHET-CNN Framework for Accurate Segmentation and Pattern Classification

Biometric and identification patterns have gained extensive research and application, particularly in iris recognition. The iris harbors a wealth of individual-specific information, making it a vital element in biometric authentication. This article presents a comprehensive study encompassing iris segmentation and identification. We introduce the Weighted Adaptive Hough Ellipsopolar Transform Convolutional Neural Network (WAHET-CNN) as a novel approach for classifying pattern images. Our experimental outcomes demonstrate a commendable 90% accuracy achieved by the proposed WAHET-CNN on the CASIA dataset Version 4.

Development of a Genetic Algorithm Based Geodesic Active Contour for Iris Based Ethnicity Prediction System

The developments in science and technology have made it possible to use biometrics in applications where it is required to establish or confirm the identity of individuals. Among all possible biometric characteristics, the use of iris texture for recognition of individuals has been proven to be highly reliable. However, existing iris prediction systems have suffered from inability to handle more constrained acquisition (processing non-ideal iris images), high processing time and inappropriate parameter settings which usually results in inaccurate segmentation and poor classification results. This research therefore developed an improved segmentation and classification algorithms for iris-based ethnicity prediction system featuring the three major tribes in Nigeria. Six hundred (600) iris images from three major tribes in Nigeria (Yoruba, Hausa and Ibo) were locally captured for the database. Genetic Algorithm based Geodesic Active Contour (GAGAC) and standard Geodesic Active Control (GAC) were used for iris segmentation while Standard Support Vector Machine (SVM) and Galactic Swarm Optimisation SVM (GSOSVM) was used for iris classification. GAGAC and GSOSVM were used in the designing of the iris-based ethnicity prediction system at segmentation and classification stage. The developed iris-based ethnicity prediction system gave an improved predictive performance over the conventional one. The developed system can be used in different areas where higher security authentication is required.

Robust iris segmentation based on ConvNeXts

Robust iris segmentation based on ConvNeXts

Robust Convolutional Neural Network Based on UNet for Iris Segmentation

Robust Convolutional Neural Network Based on UNet for Iris Segmentation

Robust and Accurate Iris Segmentation Algorithm for Color and Noisy Eye Images

Efficient and robust segmentation of iris images captured in the uncontrolled environments is one of the challenges of non-cooperative iris recognition systems. We address this problem by proposing a novel iris segmentation algorithm, which is suitable both for monochrome and color eye images. The method presented use modified Hough transform to roughly localize the possible iris and pupil boundaries, approximating them by circles. A voting mechanisms is applied to select a candidate iris regions. The detailed iris boundary is approximated by the spline curve. Its shape is determined by minimizing introduced boundary energy function. The described algorithm was submitted to the NICE.I iris image segmentation contest, when it was ranked 11th and 10th out of total 97.

KartalOl: a new deep neural network framework based on transfer learning for iris segmentation and localization task—new dataset for iris segmentation

Iris segmentation and localization in unconstrained environments are challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. Some existing methods in the literature have somehow mitigated some of the above-mentioned issues. In this paper, motivated by these weaknesses, we propose a framework that employs a deep neural network-based approach to iris segmentation and localization. The proposed framework is based on a U-Net architecture initialized with a pre-trained MobileNetV2 model. In addition, to better study the detectors in iris recognition scenarios, we have collected 1000 images. The provided dataset (KartalOl) is made publicly available for the research community. In the proposed framework, to have better generalization, we fine-tuned the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. Likewise, data augmentation techniques are applied on images. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed framework is trained and tested in CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa, along with the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The implementation source code of KartalOl is made publicly available at https://github.com/Jalilnkh/KartalOl-NIR-ISL2021031301.

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.

Double-Center-Based Iris Localization and Segmentation in Cooperative Environment with Visible Illumination.

Iris recognition has been considered as one of the most accurate and reliable biometric technologies, and it is widely used in security applications. Iris segmentation and iris localization, as important preprocessing tasks for iris biometrics, jointly determine the valid iris part of the input eye image; however, iris images that have been captured in user non-cooperative and visible illumination environments often suffer from adverse noise (e.g., light reflection, blurring, and glasses occlusion), which challenges many existing segmentation-based parameter-fitting localization methods. To address this problem, we propose a novel double-center-based end-to-end iris localization and segmentation network. Different from many previous iris localization methods, which use massive post-process methods (e.g., integro-differential operator-based or circular Hough transforms-based) on iris or contour mask to fit the inner and outer circles, our method directly predicts the inner and outer circles of the iris on the feature map. In our method, an anchor-free center-based double-circle iris-localization network and an iris mask segmentation module are designed to directly detect the circle boundary of the pupil and iris, and segment the iris region in an end-to-end framework. To facilitate efficient training, we propose a concentric sampling strategy according to the center distribution of the inner and outer iris circles. Extensive experiments on the four challenging iris data sets show that our method achieves excellent iris-localization performance; in particular, it achieves 84.02% box IoU and 89.15% mask IoU on NICE-II. On the three sub-datasets of MICHE, our method achieves 74.06% average box IoU, surpassing the existing methods by 4.64%.

AN ENHANCED ENSEMBLE HYBRID DEEP LEARNING ALGORITHM FOR IMPROVING THE ACCURACY IN IRIS SEGMENTATION

In recent years, there has been a meteoric rise in the application of deep neural networks for the purpose of iris segmentation. This can be attributed to the extraordinary capacity for learning possessed by the convolution kernels that are utilised by CNNs. Conventional methods have several drawbacks, some of which can be partially compensated for by using CNN-based algorithms, which increase the segmentation precision. On the other hand, the CNN-based iris segmentation approaches that are currently in use typically require a more complex network, which results in an increase in the number of parameters. This is essential to realise a higher degree of precision in the results. CNN-based techniques are effective, they can only be used for a specific application. This makes them inappropriate for general iris segmentation goals, even though they are effective.

Deep Learning-Based Iris Segmentation Algorithm for Effective Iris Recognition System

In this study, a 19-layer convolutional neural network model is developed for accurate iris segmentation and is trained and validated using five publicly available iris image datasets. An integrodifferential operator is used to create labeled images for CASIA v1.0, CASIA v2.0, and PolyU Iris image datasets. The performance of the proposed model is evaluated based on accuracy, sensitivity, selectivity, precision, and F-score. The accuracy obtained for CASIA v1.0, CASIA v2.0, CASIA Iris Interval, IITD, and PolyU Iris are 0.82, 0.97, 0.9923, 0.9942, and 0.98, respectively. The result shows that the proposed model can accurately predict iris and non-iris regions and thus can be an effective tool for iris segmentation.

A Symmetrical Encoder-Decoder Network with Transformer for Noise-Robust Iris Segmentation

Iris images captured in less-constrained environments are susceptible to the interference of noise such as specular reflections, eyelash and hair occlusions, motion and defocus blur, which makes it hard to accurately segment the valid iris region. To solve the problem, a symmetrical encoder-decoder network with Transformer is proposed for noise-robust iris segmentation. First, Swin Transformer is employed as the encoder to feed a sequence of the input image patches into hierarchical Transformer modules, so as to model the long-range dependencies of image pixels through self-attention mechanism, and enhance the interaction of contextual information. Secondly, a Transformer decoder which is symmetrical with the encoder is constructed, where the high-order context features extracted earlier are decoded in multiple layers. Besides, the skip connections are introduced to fuse the multi-scale features from the encoder with the up-sampled decoded features, which reduces the loss of spatial position information caused by down-sampling. Finally, supervised learning is carried out on each stage of the decoder, which improves the quality of extracted different-scaled features. A comparative experiment is carried out on three challenging noisy near-infrared (NIR) and visible (VIS) iris datasets, i.e., NICE.I, CASIA.v4-distance, and MICHE-I. Results show that the proposed method achieves better segmentation performance than several benchmark methods including traditional methods, convolutional neural network-based methods and existing Transformer-based methods on multiple evaluation metrics like <italic>E</italic>₁, <italic>E</italic>₂, <italic>F</italic>₁, and MIOU, and particularly demonstrates significant advantages in reducing the interference of adverse noise. The iris recognition experiment on the CASIA.v4-distance dataset also shows that the proposed method can effectively improve the performance of iris recognition, suggesting a good application potential.

Research on strabismus iris segmentation model based on deep snake multitask learning

Biometrics is widely used in various fields as a means of biological individual identification, and iris recognition is recognized as an efficient, stable, and secure biometric method. However, when the cooperation with users is reduced under complex scenes, the collected iris images are prone to squint or defocus, which causes serious difficulties for iris preprocessing, especially iris location, and segmentation. To solve this problem, we propose a multitask deep learning model based on deep snake. Using the closed-loop prior conditions of the inner and outer boundaries of the iris itself, the active contour method is used to achieve accurate iris location, and semantic segmentation is carried out in the effective region. First, the initial iris location and segmentation results are obtained using the target detection module based on mask regions with convolutional neural networks, then the initial contour is located by poles. The iris boundary is represented by a set of ordered point coordinates through equal spacing sampling. The initial contour is transferred as input to the active contour fitting network based on deep snake to obtain accurate iris location results. Finally, the segmentation results are obtained by the internal and external boundary constraint models. In the segmentation process, efficient channel attention mechanism is introduced to improve the segmentation accuracy and make the network focus on the effective iris region. The SoftPool layer is used to reduce the loss of image detail information caused by pooling, so that the whole network is differentiable. The performance improvements of 3.26%, 3.9%, and 3.45% are obtained on the three squint iris datasets, respectively, which proves the effectiveness of the model in solving the squint iris problem and improves the accuracy and robustness of iris recognition in complex scenes.

Accurate iris segmentation and recognition using an end-to-end unified framework based on MADNet and DSANet

Due to their insufficient generalization ability, iris segmentation algorithms based on deep learning cannot accurately segment iris images without corresponding ground truth (GT) data. Moreover, prior to recognition, the segmented image requires normalization to reduce the influence of pupil deformation. However, normalization of nonconnected iris regions will introduce noise, thereby decreasing the recognition rate. This paper proposes an end-to-end unified framework based on deep learning that does not include normalization in order to achieve improved accuracy in iris segmentation and recognition. In this framework, a multiattention dense connection network (MADNet) and dense spatial attention network (DSANet) are designed for iris segmentation and recognition, respectively. Finally, numerous ablation experiments are conducted to demonstrate the effectiveness of MADNet and DSANet. Experiments on three employed databases show that our proposed method achieves the best segmentation and recognition performance on low-quality iris images without corresponding GT data.

LiSeNet: multitask lightweight segmentation network for accurate and complete iris segmentation

The demand for applying the iris segmentation model on mobile devices has been growing rapidly. Most current segmentation networks have an enormous amount of parameters, hence unsuitable for mobile devices, while other small memory footprint models follow the spirit of classification networks and ignore the inherent characteristic of segmentation. To address the challenge, we propose a lightweight segmentation network (LiSeNet) for iris segmentation of noisy images. Unlike previous studies that only focus on improving the accuracy of segmentation masks, LiSeNet can simultaneously obtain segmentation masks, parameterized pupillary and limbic boundaries of the iris, further enabling CNN-based iris segmentation to be applied in any regular iris recognition systems. We first propose a multiscale concatenate (MSC) Block, which connects multiple sizes of convolution kernels in a dense manner, gradually reduces the dimension of feature maps and uses the aggregation of them for image representation. Based on the MSC block, we develop a two-stage refinement encoder to aggregate discriminative features through subnetwork feature reuse and substage feature reassess, thus obtaining a sufficient receptive field and enhancing the model learning ability. To exploit object contextual information more efficiently, we further devise a grouped spatial attention to emphasize the important features and suppress irrelevant noises through a gating mechanism in the decoder. Extensive experiments on three challenging iris datasets show that LiSeNet, without any complicated postprocessing, achieves competitive or state-of-the-art performance with only 2.2M parameters, being 14 × smaller than the previous best method. Code will be publicly available.

Noisy iris smoothing and segmentation scheme based on improved Wildes method.

In an automated iris recognition system, in order to get higher accuracy, we should have an efficient iris segmentation process. The reliability of accurate “iris recognition” system largely depends on the accuracy of segmentation process. Traditional “iris segmentation” methods are unable to detect the exact boundaries of iris and pupil, which is time consuming and also highly sensitive to noise. To overcome these problems, we have proposed an improved Wildes method (IWM) for segmentation in iris recognition system. The proposed algorithm consists of two major steps before applying Wildes method for segmentation: edge detection of iris and pupil from a noisy eye image with improved Canny with fuzzy logic (ICWFL) and removal of unwanted noise from above step with a hybrid restoration fusion filter (HRFF). A comparative study of various edge detection techniques is performed to prove the efficiency of ICWFL method. Similarly, the proposed method is tested with various noise densities from 10 to 95 dB. Also the working of the proposed HRFF is compared with some existing smoothing filters. Various experiments have been performed with the help of iris database of IIT_Delhi. Both visual and numerical results prove the efficiency of the proposed algorithm.

Towards More Accurate and Complete Heterogeneous Iris Segmentation Using a Hybrid Deep Learning Approach

Accurate iris segmentation is a crucial preprocessing stage for computer-aided ophthalmic disease diagnosis. The quality of iris images taken under different camera sensors varies greatly, and thus accurate segmentation of heterogeneous iris databases is a huge challenge. At present, network architectures based on convolutional neural networks (CNNs) have been widely applied in iris segmentation tasks. However, due to the limited kernel size of convolution layers, iris segmentation networks based on CNNs cannot learn global and long-term semantic information interactions well, and this will bring challenges to accurately segmenting the iris region. Inspired by the success of vision transformer (VIT) and swin transformer (Swin T), a hybrid deep learning approach is proposed to segment heterogeneous iris images. Specifically, we first proposed a bilateral segmentation backbone network that combines the benefits of Swin T with CNNs. Then, a multiscale feature information extraction module (MFIEM) is proposed to extract multiscale spatial information at a more granular level. Finally, a channel attention mechanism module (CAMM) is used in this paper to enhance the discriminability of the iris region. Experimental results on a multisource heterogeneous iris database show that our network has a significant performance advantage compared with some state-of-the-art (SOTA) iris segmentation networks.

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.