Heterogeneous Iris Research Articles

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.

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.

Heterogeneous Noise Iris Segmentation Based on Attention Mechanism and Dense Multiscale Features

Heterogeneous Noise Iris Segmentation Based on Attention Mechanism and Dense Multiscale Features

The Two-Stage Recognition Method Based on Texture Signals of the Heterogeneous Unsteady Iris

In this paper, a two-stage multi-category recognition structure based on texture features is proposed. This method can solve the problem of the decline in recognition accuracy in the scene of lightweight training samples. Besides, the problem of recognition effect different in the same recognition structure caused by the unsteady iris can also be solved. In this paper’s structure, digitized values of the edge shape in the iris texture of the image are set as the texture trend feature, while the differences between the gray values of the image obtained by convolution are set as the grayscale difference feature. Furthermore, the texture trend feature is used in the first-stage recognition. The template category that does not match the tested iris is the elimination category, and the remaining categories are uncertain categories. Whereas, in the second-stage recognition, uncertain categories are adopted to determine the iris recognition conclusion through the grayscale difference feature. Then, the experiment results using the JLU iris library show that the method in this paper can be highly efficient in multi-category heterogeneous iris recognition under lightweight training samples and unsteady state.

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.

PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris

Current segmentation methods have limitations for multi-source heterogeneous iris segmentation since differences of acquisition devices and acquisition environment conditions lead to images of greatly varying quality from different iris datasets. Thus, different segmentation algorithms are generally applied to distinct datasets. Meanwhile, deep-learning-based iris segmentation models occupy more space and take a long time. Therefore, a lightweight, precise, and fast segmentation network model, PFSegIris, aimed at the multi-source heterogeneous iris is proposed by us. First, the iris feature extraction modules designed were used to fully extract heterogeneous iris feature information, reducing the number of parameters, computation, and the loss of information. Then, an efficient parallel attention mechanism was introduced only once between the encoder and the decoder to capture semantic information, suppress noise interference, and enhance the discriminability of iris region pixels. Finally, we added a skip connection from low-level features to catch more detailed information. Experiments on four near-infrared datasets and three visible datasets show that the segmentation precision is better than that of existing algorithms, and the number of parameters and storage space are only 1.86 M and 0.007 GB, respectively. The average prediction time is less than 0.10 s. The proposed algorithm can segment multi-source heterogeneous iris images more precisely and quicker than other algorithms.

Multi-Source Heterogeneous Iris Recognition Using Stacked Convolutional Deep Belief Networks-Deep Belief Network Model

With the development of iris recognition technology, sensors of iris images acquisition are being constantly developed and updated. Re-register users every time a new sensor is deployed is time-consuming and complicated, especially in applications with large-scale registered users. Therefore, it is a challenging problem to choose the common recognition model which is effective for multi-source heterogeneous iris recognition(MSH-IR). The paper proposes a efficient neural network model of stacked Convolutional Deep Belief Networks-Deep Belief Network (CDBNs-DBN) for MSH-IR. The main improvements are two parts: firstly, this model uses the region-by-region extraction method and positions the convolution kernel through the offset of the hidden layer to locate the effective local texture feature structure. Secondly, the model uses DBN as a classifier in order to reduce the reconstruction error through the negative feedback mechanism of the auto-encoder. Experimental results have been implemented on publicly available IIT Delhi iris database, which is captured by three different iris captured sensors. Experiments shows the model performs strong robustness performance and recognition ability.

Heterogeneous iris certification method with universal sensors and safety output based on multisource fusion features and entropy labels of lightweight samples

Current iris recognition technology faces practical difficulties. For example, the unsteady morphology of a heterogeneous iris is generated by a variety of different devices and environments. The existing iris data sets have difficulty meeting the requirements of learning methods under a single deep learning framework. Research on iris recognition security has limited response to results stealing. Therefore, we propose a one-to-one heterogeneous iris certification method with universal sensors and safety output based on multisource fusion features and entropy labels of lightweight samples. This method is based on the classic convolutional neural network structure, and a lightweight neural network certification structure is designed. At first, convert the constrained multistate iris image into the digital features based on statistical learning and multisource feature fusion mechanism. The information entropy of the iris feature label is used to set the iris entropy feature category label and design a certification function that meets the requirements of different acquisition sensors according to the category label to obtain the certification result. Through the result encryption output module, the security output is achieved between the user and the certification result, and measures can be taken in time to confirm the stealing attack to improve the security of the output of certification result. As the requirement for the number and quality of irises changes, the category labels in the certification function are dynamically adjusted using a feedback learning mechanism. Three different acquisition sensors in the JLU iris library are used to do the experiments. The results prove that, for lightweight constrained multistate irises, the above mentioned problems are ameliorated to a certain extent by this method.

Heterogeneous Iris One-to-One Certification with Universal Sensors based On Quality Fuzzy Inference and Multi-Feature Fusion Lightweight Neural Network.

Due to the unsteady morphology of heterogeneous irises generated by a variety of different devices and environments, the traditional processing methods of statistical learning or cognitive learning for a single iris source are not effective. Traditional iris recognition divides the whole process into several statistically guided steps, which cannot solve the problem of correlation between various links. The existing iris data set size and situational classification constraints make it difficult to meet the requirements of learning methods under a single deep learning framework. Therefore, aiming at a one-to-one iris certification scenario, this paper proposes a heterogeneous iris one-to-one certification method with universal sensors based on quality fuzzy inference and a multi-feature entropy fusion lightweight neural network. The method is divided into an evaluation module and a certification module. The evaluation module can be used by different devices to design a quality fuzzy concept inference system and an iris quality knowledge concept construction mechanism, transform human logical cognition concepts into digital concepts, and select appropriate concepts to determine iris quality according to different iris quality requirements and get a recognizable iris. The certification module is a lightweight neural network based on statistical learning ideas and a multi-source feature fusion mechanism. The information entropy of the iris feature label was used to set the iris entropy feature category label and design certification module functions according to the category label to obtain the certification module result. As the requirements for the number and quality of irises changes, the category labels in the certification module function were dynamically adjusted using a feedback learning mechanism. This paper uses iris data collected from three different sensors in the JLU (Jilin University) iris library. The experimental results prove that for the lightweight multi-state irises, the abovementioned problems are ameliorated to a certain extent by this method.

Multi-Source Feature Fusion and Entropy Feature Lightweight Neural Network for Constrained Multi-State Heterogeneous Iris Recognition

Current iris recognition technology faces practical difficulties. For example, due to the unsteady morphology of a heterogeneous iris generated by a variety of different devices and environments, the traditional processing methods of statistical learning or cognitive learning for a single iris source are not effective. The existing iris data set size and situational classification constraints make it difficult to meet the requirements of learning methods under a single deep learning framework. Therefore, this paper proposes a method of heterogeneous iris recognition based on an entropy feature lightweight neural network under multi-source feature fusion. The method is divided into an image-processing module and a recognition module. The image-processing module converts the iris image into a recognition label via a convolutional neural network. The recognition module is based on statistical learning ideas and design of a multi-source feature fusion mechanism. The information entropy of the iris feature label is used to set the iris entropy feature category label and design the recognition function according to the category label to obtain the recognition result. As the requirement for the number and quality of irises changes, the category labels in the recognition function are dynamically adjusted using a feedback learning mechanism. This paper uses iris data collected from three different devices in the JLU iris library. The experimental results prove that for multi-category classification of lightweight constrained multi-state irises, the abovementioned problems are ameliorated to a certain extent by this method.

Enhancing human iris recognition performance in unconstrained environment using ensemble of convolutional and residual deep neural network models

Despite the prominent advancements in iris recognition, unconstrained image acquisition through heterogeneous sensors has been a major obstacle in applying it for large-scale applications. In recent years, deep convolutional networks have achieved remarkable performance in the field of computer vision and have been employed in iris applications. In this study, three distinct models based on the ensemble of convolutional and residual blocks are proposed to enrich heterogeneous (cross-sensor) iris recognition. In order to analyze their quantitative performances, extensive experiments are carried out on two publicly available iris databases, ND-iris-0405 dataset and ND-CrossSensor-Iris-2013 dataset. Further, the final model has been scrutinized based on the least error rate and then fused using score-level fusion with two preeminent feature extraction methods, i.e., scale-invariant feature transform and binarized statistical information features. The resultant model is examined for cross-sensor iris recognition and reported the top two error rates as 1.01% and 1.12%. It infers that the proposed approach constitutes vital discerning iris features and can recognize that the micro-patterns exist inside the iris region. Furthermore, a comparative study is carried out with the state of the art, where the proposed approach obtains significantly improved performance.

Optimized robust multi-sensor scheme for simultaneous video and image iris recognition

Today, recognition of people by the iris is widely used when secure identification of a person is needed. Iris biometric identification systems should be able to work with heterogeneous iris images captured by different types of iris sensors. However, stable iris recognition systems that are effective for all types of iris cameras are not readily available. These systems should also be able to work simultaneously with images and video frames. In this work, we present an optimized robust multi-sensor scheme with a strategy that combines video frame quality evaluation with robust fusion methods at segmentation level for simultaneous video and image iris recognition. As part of the proposed scheme, we presented a Modified Laplacian Pyramid-based fusion method at segmentation stage. Experimental results on the Casia-V3-Interval, Casia-V4-Thousand, Ubiris-V1 and MBGC-V2 databases show that the optimized robust scheme increases recognition accuracy, and is robust to different types of iris sensors and able to simultaneously work with video and images.

A Code-Level Approach to Heterogeneous Iris Recognition

Matching heterogeneous iris images in less constrained applications of iris biometrics is becoming a challenging task. The existing solutions try to reduce the difference between heterogeneous iris images in pixel intensities or filtered features. In contrast, this paper proposes a code-level approach in heterogeneous iris recognition. The non-linear relationship between binary feature codes of heterogeneous iris images is modeled by an adapted Markov network. This model transforms the number of iris templates in the probe into a homogenous iris template corresponding to the gallery sample. In addition, a weight map on the reliability of binary codes in the iris template can be derived from the model. The learnt iris template and weight map are jointly used in building a robust iris matcher against the variations of imaging sensors, capturing distance, and subject conditions. Extensive experimental results of matching cross-sensor, high-resolution versus low-resolution and, clear versus blurred iris images demonstrate the code-level approach can achieve the highest accuracy in compared with the existing pixel-level, feature-level, and score-level solutions.

DeepIris: Learning pairwise filter bank for heterogeneous iris verification

Heterogeneous iris recognition (HIR) is in great demand for a large-scale identity management system. Iris images acquired in heterogeneous environment have large intra-class variations, such as different resolutions or different sensor optics, etc. Therefore, it is challenging to manually design a robust encoding filter to face the complex intra-class variations of heterogeneous iris images. This paper proposes a deep learning based framework for heterogeneous iris verification, namely DeepIris, which learns relational features to measure the similarity between pairs of iris images based on convolutional neural networks. DeepIris is a novel solution to iris recognition in two main aspects. (1) DeepIris learns a pairwise filter bank to establish the relationship between heterogeneous iris images, where pairs of filters are learned from two heterogeneous sources. (2) Different from two separate steps in terms of handcrafted feature extraction and feature matching in conventional solutions, DeepIris directly learns a nonlinear mapping function between pairs of iris images and their identity supervision with a pairwise filter bank (PFB) from different sources. Thus, the learned pairwise filters can adapt to new sources when given new training data. Extensive experimental results on the Q-FIRE and the CASIA cross sensor datasets demonstrate that EER (Equal Error Rate) of heterogeneous iris verification is reduced by 90% using DeepIris compared to traditional methods.