Iris Feature Research Articles

Microsaccades: Empirical Research and Methodological Advances - Introduction to Part 1 of the Thematic Special Issue.

Microsaccades: Empirical Research and Methodological Advances - Introduction to Part 1 of the Thematic Special Issue.

In Vivo Analysis and Comparison of Anterior Segment Structures of Both Eyes in Unilateral Herpetic Anterior Uveitis

ABSTRACT Purpose: To compare the Schlemm’s canal area (SCA) and to define iris features in patients with unilateral herpetic anterior uveitis (HAU) by using anterior segment spectral-domain optical coherence tomography (AS SD-OCT). Methods: Unilateral HAU cases that had been in complete remission for ≥ 3 months were included. Two investigators analyzed the iris features of HAU. SCA in both healthy (N), and affected eyes (HAU) were compared. Results: The mean age of the participants (n = 22) was 36.3 ± 15 (17–70) years. The mean nasal and temporal (n-t) SCA was measured as 10,844 ± 4806 µm2 and 8,772 ± 3138 µm2 in HAU in comparison with 10,200 ± 4,824 µm2 and 10,045 ± 3,889 µm2 in N, respectively (p = .47 and p = .12, respectively). The most common iris features were the sectoral iridoplegia (100%) on biomicroscopy and the disorganization of the iris layers (DIL) (77%) on AS SD-OCT images. Conclusion: SCA found similar on both sides. DIL is the most common iris feature of affected eyes.

Robust Iris Presentation Attack Detection Fusing 2D and 3D Information

Diversity and unpredictability of artifacts potentially presented to an iris sensor calls for presentation attack detection methods that are agnostic to specificity of presentation attack instruments. This article proposes a method that combines two-dimensional and three-dimensional properties of the observed iris to address the problem of spoof detection in case when some properties of artifacts are unknown. The 2D (textural) iris features are extracted by a state-of-the-art method employing Binary Statistical Image Features (BSIF) and an ensemble of classifiers is used to deliver 2D modality-related decision. The 3D (shape) iris features are reconstructed by a photometric stereo method from only two images captured under near-infrared illumination placed at two different angles, as in many current commercial iris recognition sensors. The map of normal vectors is used to assess the convexity of the observed iris surface. The combination of these two approaches has been applied to detect whether a subject is wearing a textured contact lens to disguise their identity. Extensive experiments with NDCLD'15 dataset, and a newly collected NDIris3D dataset show that the proposed method is highly robust under various open-set testing scenarios, and that it outperforms all available open-source iris PAD methods tested in identical scenarios. The source code and the newly prepared benchmark are made available along with this article.

Constrained unsteady-state iris fast certification for lightweight training samples based on the scale change stable feature and multi-algorithm voting.

Aiming at the problem of fast certification for a constrained iris in the same category caused by the unstable iris features caused by the change of the iris acquisition environment and shooting status under lightweight training samples, a one-to-one fast certification algorithm for constrained unsteady-state iris based on the scale change stable feature and multi-algorithm voting is proposed. Scale change stable features are found by constructing an isometric differential Gaussian space, and a local binary pattern algorithm with extended statistics (ES-LBP), the Haar wavelet with over threshold detection and the Gabor filter algorithm with immune particle swarm optimization (IPSO) are used to represent the stable features as binary feature codes. Iris certification is performed by the Hamming distance. According to the certification results of three algorithms, the final result is obtained by multi-algorithm voting. Experiments with the JLU and CASIA iris libraries under the iris prerequisite conditions show that the correct recognition rate of this algorithm can reach a high level of 98% or more, indicating that this algorithm can improve the operation speed, accuracy and robustness of certification.

Development of Artificial Bee Colony Algorithm for Iris Segmentation

Iris segmentation is a crucial process in the iris recognition that it locates and captures the unique features inside the human iris image. However, iris image contains high noise rate and sometimes a genuine user is recognized as non-genuine by computer system. An algorithm from swarm-based intelligence is demanded for natural search images. Therefore, an experimental investigation was conducted to explore artificial bee colony (ABC) algorithm for iris segmentation. The ABC algorithm measures the performance of convergence in learning the authentic iris features. Based on the findings, the average fitness is always less than or equal to the best fitness, and the differences between the two goes on decreasing over time, which is until the algorithm completely converges. The parameters of ABC Algorithm are programmed with at best 100 times of calculation to collect the best iteration and best fitness. In fact, results showed that ABC uses the population for every iteration instead of a single iteration. Number of iterations directly affect the time of optimal search for ideal convergence, which meets the aim of this study. As a conclusion, if the mean and standard deviation is low in values, it is proven that the speed of searching the iris features is faster and more robust.

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.

Iris Feature Extraction for Personal Authentication Using Macro Features

Iris Feature Extraction for Personal Authentication Using Macro Features

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.

Privacy-preserving iris authentication using fully homomorphic encryption

Rapid advancement in technology has led to the use of biometric authentication in every field. In particular, from the past few years, iris recognition systems has gained overwhelming advancement over other biometric traits due to its stability and uniqueness. Directly storing the templates into a centralized server leads to privacy concerns. Many state-of-the-art iris authentication systems based on cancelable biometrics and bio-cryptosystems have been introduced to provide security for the iris templates. However, these works suffer from accuracy loss relative to unprotected systems, or they require auxiliary data (AD), which compromise the privacy of the templates and security of the system. To address this, we propose a novel privacy-preserving iris authentication using fully homomorphic encryption which ensures the confidentiality of the templates and restricts the leakage of data from the templates. Our method improves the recognition accuracy by generating rotation invariant iris codes and reduces the computational time by using the batching scheme. Our approach satisfies all the requirements specified in the ISO/IEC 24745 standard. The proposed method has experimented on four benchmark publicly available iris databases which illustrate that our method can be practically achievable with no loss in the accuracy and preserve the privacy of the iris templates. Our method encrypts and computes the Hamming distance of 2560-dimensional iris features in about 0.0185 seconds only with an equal error rate value of 0.19% for CASIA-V 1.0 database.

Template Matching Analysis using Neural Network for Mobile Iris Recognition System

Today, the use of mobile phone among individuals with an advanced technology, acquisition and computation resources is crucial in securing personal data and services as well as in identifying a person. Furthermore, there has been growing interest of researchers using mobile as an acquisition device to capture an eye image in a non-cooperative environment (static motion and at different distances) for iris recognition. However, the use of mobile as an acquisition device still facing some noise factors because the quality of image captured in this environmental condition is low compared to a camera device. Besides, low awareness of user in handling the mobile devices and lack technical experiences in capturing iris images with this environmental condition is generally uncontrolled contribute to producing unpredictable low quality of eye images. Due to these issues, it led to incorrect segmentation of iris boundaries and subsequently lowered the ability to match the iris features. Hence, this condition contributes to performance degradation of iris recognition system. Therefore, to improve the ability to match the iris features in mobile iris recognition system, a combination with neural network is proposed. The iris database used to test against these methods is CASIA-Iris-M1-S2. The proposed method, a combination of Hamming distance and neural network has achieved a better result where it increases the existing method in term of accuracy for mobile iris recognition under non-cooperative environment.

Fusion Time Reduction of a Feature Level Based Multimodal Biometric Authentication System

In the present study, a multimodal biometric authentication method is presented to confirm the identity of a person based on his face and iris features. This method depends on multiple biometric techniques that combine face and iris (left and right) features to recognize. The authors have designed and applied a system to identify people. It depends on extracting the features of the face using Rectangle Histogram of Oriented Gradient (R-HOG). The study applies a feature-level fusion using a novel fusion method which employs both the canonical correlation process and the proposed serial concatenation. A deep belief network was used for the recognition process. The performance of the proposed systems was validated and evaluated through a set of experiments on SDUMLA-HMT database. The results were compared with others, and have shown that the fusion time has been reduced by about 34.5%. The proposed system has also succeeded in achieving a lower equal error rate (EER), and a recognition accuracy up to 99%.

Analyses of morphobiochemical characteristics of the Iris genus within the conditions of the Kuzbass botanic garden

The results of study of adaptation potential of plants of the Iris genus of Iridaceae within the conditions of the Kuzbass botanic garden are represented in the article. It was made the evaluation of Morphobiologic characteristics of irises, the terms of flowering was determined together with duration and productivity of flowering, different capacity to fructification was marked, and the sums of positive temperatures need to such phonological phases of development, as growth, flower-bud formation, flowering and fructification were determined. The overall estimate over the complex of decorative signs showed that they are original and stable in the local conditions and may be recommended for commercial floriculture and greening within the conditions of Kemerovo region.Studied an indicating role of phenol compounds, peroxidases, ascorbic acid in Iris leaves during seasonal development. Analyses of studies revealed that the level of biochemical values in the leaves of decorative perennials growing on the territory of the Kuzbass botanic garden depended from species peculiarities and vegetation period of plants. Within the period of vegetation the most accumulation of ascorbic acid was revealed. The studied figures may be used as an informative parameter for the plants state evaluation for further phytoindication and introduction.

T-Center: A Novel Feature Extraction Approach Towards Large-Scale Iris Recognition

For large-scale iris recognition tasks, the determination of classification thresholds remains a challenging task, especially in practical applications where sample space is growing rapidly. Due to the complexity of iris samples, the classification threshold is difficult to determine with the increase of samples. The key issue to solving such threshold determination problems is to obtain iris feature vectors with more obvious discrimination. Therefore, we train deep convolutional neural networks based on a large number of iris samples to extract iris features. More importantly, an optimized center loss function referred to Tight Center (T-Center) Loss is used to solve the problem of insufficient discrimination caused by the traditional Softmax loss function. In order to evaluate the effectiveness of our proposed method, cosine similarity is used to estimate the similarity between the features on the published iris recognition datasets ND-IRIS-0405, CASIA-Thousand and IITD. Our experiment results prove that the T-Center loss can minimize intra-class variance and maximize inter-class variance, which achieve significant performance on the benchmark experiments.

Driver Fatigue Detection Method Based on Eye States With Pupil and Iris Segmentation

Fatigue driving has become one of the most common causes for traffic accidents. In this article, we proposed an effective fatigue detection method based on eye status with pupil and iris segmentation. The segmented feature map can guide the detection to focus on pupil and iris. A streamlined network, consisting of a segmentation network and a decision network, is designed, which greatly improves the accuracy and generalization of eye openness estimation. Specifically, the segmentation network that uses light U-Net structure performs a pixel-level classification on the eye images, which can accurately extract pupil and iris features from the video’s images. Then, the extracted feature map is used to guide the decision network to estimate eye openness. Finally, the detection method is test by the National Tsing Hua University Drowsy Driver Detection (NTHU-DDD) Video Dataset and the precision of fatigue detection achieves 96.72%. Experimental results demonstrate that the proposed method can accurately detect the driver fatigue in-time and possesses superior accuracy over the state-of-the-art techniques.

Sub-band-based feature fusion and hybrid fusion approaches for multimodal biometric identification

A multimodal biometric system using feature fusion and hybrid fusion of face and iris is proposed. A novel feature level fusion of face and iris features, using both low and high frequency sub-bands of discrete wavelet transform (DWT), and principal component analysis (PCA) is designed. The redundant data resulted from feature fusion of face and iris is overcome by feature transformation through linear discriminant analysis (LDA). The proposed feature level fusion is tested for face databases (ORL and Yale), and iris databases (CASIA and UBIRIS). The performance of the proposed feature level fusion approach is superior to DWT, PCA and Gabor+PCA-based fusion methods by exhibiting highest recognition rate of 97% with low dimensionality. Further, a hybrid fusion of feature level and score level fusion methods is proposed to improve the performance of the multimodal biometric system. In comparison to feature level and score level fusion methods, the hybrid fusion method attains highest recognition rate of 99.6% and least equal error rate (EER) of 0.086 for ORL+CASIA database.

Sub-band-based feature fusion and hybrid fusion approaches for multimodal biometric identification

A multimodal biometric system using feature fusion and hybrid fusion of face and iris is proposed. A novel feature level fusion of face and iris features, using both low and high frequency sub-bands of discrete wavelet transform (DWT), and principal component analysis (PCA) is designed. The redundant data resulted from feature fusion of face and iris is overcome by feature transformation through linear discriminant analysis (LDA). The proposed feature level fusion is tested for face databases (ORL and Yale), and iris databases (CASIA and UBIRIS). The performance of the proposed feature level fusion approach is superior to DWT, PCA and Gabor+PCA-based fusion methods by exhibiting highest recognition rate of 97% with low dimensionality. Further, a hybrid fusion of feature level and score level fusion methods is proposed to improve the performance of the multimodal biometric system. In comparison to feature level and score level fusion methods, the hybrid fusion method attains highest recognition rate of 99.6% and least equal error rate (EER) of 0.086 for ORL+CASIA database.

Face–Iris Multimodal Biometric Identification System

Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an efficient multi-resolution 2D Log-Gabor filter to capture textural information in different scales and orientations. On the other hand, the facial features are computed using the powerful method of singular spectrum analysis (SSA) in conjunction with the wavelet transform. SSA aims at expanding signals or images into interpretable and physically meaningful components. In this study, SSA is applied and combined with the normal inverse Gaussian (NIG) statistical features derived from wavelet transform. The fusion process of relevant features from the two modalities are combined at a hybrid fusion level. The evaluation process is performed on a chimeric database and consists of Olivetti research laboratory (ORL) and face recognition technology (FERET) for face and Chinese academy of science institute of automation (CASIA) v3.0 iris image database (CASIA V3) interval for iris. Experimental results show the robustness.

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.

An End to End Deep Neural Network for Iris Recognition

Abstract The application of biometrics technology in all areas of people’s lives in today’s intelligent era. With the advantages of high accuracy and contactless, iris recognition is an important and challenging research area. In this work, an application of the combined network model based on EfficinetNet-b0 is presented in iris recognition, which integrates iris segmentation, normalization, iris feature extraction and matching into a unified network. The network model has high parameter efficiency and speed. Compared with previous deep iris recognition network, the network architecture has three characteristics: (1) Compared with most existing training and phase adjustment algorithms, it is end-to-end trainable. (2) Grad-cam has class recognition and high resolution. It provides a good visual interpretation. (3) An effective and smaller baseline model is proposed that balances the depth, width and resolution of the network based on the scaling model and achieves better results. The hybrid iris databases, composed of CASIA Thousand and Mmu2, proves that the accuracy and efficiency of the composite network framework are better than those of the previous network framework. The visualization of data sets is validated, which proves that the combined model is robust to iris image localization.

Iris Feature-Based 3-D Gaze Estimation Method Using a One-Camera-One-Light-Source System

Multicamera or multilight-source is generally used to estimate the 3-D coordinates of the cornea center for 3-D gaze estimation in the existing gaze trackers. Although some model-based methods use one-camera systems to estimate 3-D gazes, which include some user-dependent eye parameters preset by fixed values, a one-camera-one-light-source system is still unable to achieve 3-D gaze estimation by considering individual differences. In this article, an iris feature-based method using one camera and one light source for 3-D gaze estimation is proposed. The iris radius and the kappa angle are determined during user calibration. The optical axis is reconstructed by the iris center and its normal vector from only the iris features. Therefore, with the real-time estimation of kappa angle, the 3-D gaze is estimated by an optimization method. Computer simulations and practical experiments have been performed to analyze the feasibility and robustness of the proposed method. This article's innovation lies in achieving 3-D gaze estimation based on only one-camera and one-light source with calibrated eye-specific parameters, which breaks through the limitation of the existing methods using a 3-D cornea center on the system's hardware requirements of 3-D gaze estimation. The simplified hardware system has great application values, especially in the portable mobile devices widely used today. Moreover, a binocular model is used to optimize the results of the point of regard, which improves the accuracy of 3-D gaze estimation effectively.