Non-cooperative Iris Recognition Research Articles

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.

Accurate Iris Segmentation for Non-Cooperative Iris Recognition through Multi-task Network

Iris image suffer from noise due to non-cooperative situation. It creates challenge for segmentation. Here we propose deep learning based efficient segmentation method. It provides complete solution for such situation. To train this we have used various databases, which involved various illumination and imaging sensors. The proposed segmentation method can be utilized for all iris recognition methodology to improve performance in non-cooperative iris recognition. Key words: Iris Segmentation, multi task learning, iris localization, iris recognition

Non-Cooperative Iris Segmentation: A Two-Stage Approach

Aim: The accuracy of the non-cooperative iris recognition is highly dependent on the proper segmentation of the iris region from the input eye image. The traditional non-cooperative iris segmentation algorithms decrease significantly because of several noise factors such as specular reflections, occlusions, eyelashes, and eyelids. However, several techniques are developed to overcome these drawbacks in the iris segmentation process; it is still a challenging task to localize the iris texture regions. Background: Recently, segmentation of iris image is the most important process in a robust iris recognition system due to the images captured from non-cooperative environments which introduce occlusions, blur, specular reflections, and off-axis. However, several techniques are developed to overcome these drawbacks in the iris segmentation process; it is still a challenging task to localize the iris texture regions. In this research, an effective two-stage of iris segmentation technique is proposed in a non-cooperative environment. Objective: To proposed an effective two-stage of iris segmentation technique in a non-cooperative environment. Methods: Modified Geodesic Active Contour-based level set segmentation with Particle Swarm Optimization (PSO) is employed for iris segmentation. In this, the PSO algorithm is used to minimize the energy of the gradient descent equation in a region-based level set segmentation algorithm. The global threshold-based segmentation (enhanced Otsu’s method) is employed for pupil region segmentation. Results: The experiment considered two well-known databases such as UBIRIS.V1 and UBIRIS.V2. The simulation outcomes demonstrate that the proposed novel approach attained more accurate and robust iris segmentation under non-cooperative conditions. Also, the results of the modified Geodesic Active Contour-based level set segmentation with the PSO algorithm attained better results than the conventional segmentation techniques. Conclusion: An effective two-stage of iris segmentation is proposed in a non-cooperative environment using Geodesic active contour-based level set segmentation with particle swarm optimization and an enhanced Otsu method is employed for pupil segmentation. The proposed two-stage of iris segmentation technique segments the iris in its genuine shape which will provide a more robust and precise iris recognition system.

Optimization method for non-cooperative iris recognition task using Daugman integro-differential operator

We considered the issues arising when Daugman’s integrodifferential operator is used to localize iris edges. Daugman’s integro-differential operator is a widely common method to localize the iris, however, problems of the operator optimization poorly presented in the literature. The article considers existing methods of optimization of the operator applied at the iris recognition step. Based on the provided research we proposed using Nelder-Mead and Differential Evolution methods to optimize the integro-differential operator. The problems of the iris localization and their solving methods were considered. The article focuses attention on non-cooperative iris recognition. The very general boundary conditions based on iris’s anatomy which are not dependent on captured image properties were defined. The results of the comparative analysis of the accuracy and performance of selected optimization methods of Daugman’s integrodifferential operator were presented at the experimental results chapter. It was found that the Differential evolution optimization method gives fine performance and correctness. It was concluded that the Differential evolution is expedient as Daugman’s integro-differential operator optimization method.

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.

A Segmentation Perspective of Non-Cooperative Iris Recognition

Iris Segmentation, an initial and vital stage of the iris recognition stage which directly affects the recognition accuracy. Especially, the non-cooperative environment that leads to contain many of the noise parameters in the captured image. Since the recognition accuracy of the iris biometrics system is extremely dependent on the proper iris segmentation, this paper is devoted to the segmentation perspective of the non-cooperative iris recognition system. The initial stage of the proposed method is started with applying a hybrid median filter algorithm to remove the possible noises and then a region-based level set algorithm is applied to overcome the identification of the concave property in the non-cooperative iris segmentation and enhanced Otsu’s thresholding method is applied to the pupil segmentation. UBIRIS, a publicly available iris database for the non-cooperative situation, Version 1 and Version 2 is used for the implementation purpose. The accuracy of the segmentation result is achieved as 94.56 and 94.53 for the UBIRISv.1 and UBIRISv.2 respectively which show the proposed method as a better one.

An effective non-cooperative iris recognition system using hierarchical collaborative representation-based classification

In recent years, non-cooperative iris recognition has gained a major role in biometric authentication system. However, owing to images captured in non-cooperative environments, it is quite a difficult job, for these images have specular reflections, blur, occlusions, and are off-axis. This research introduces an efficient non-cooperative iris recognition system developed for hierarchical collaborative representation-based classifier (HCRC). The proposed method includes three stages. The first stage involves image preprocessing. Initially, hybrid median filtering is done to reduce noise and to improve the image quality. Then, segmentation of the abnormal non-cooperative iris image is carried out by applying Geodesic Region-based Active Contour Level-set algorithm and threshold-based segmentation algorithm. In the second stage, 2 × 2 block-based Local Ternary Pattern (LTP) is applied to the segmented image. This gives upper and lower LTP histogram blocks. The final feature vector is obtained by combining these two blocks. In the third stage, the feature vectors are applied to the HCRC for classification on the basis of the iris database. The proposed iris recognition technique proved itself by achieving 98.60, 0.095 and 0.096 accuracy, false acceptance rate and false rejection rate, respectively.

Towards Enhancing Non-Cooperative Iris Recognition using Improved Segmentation Methodology for Noisy Images

Towards Enhancing Non-Cooperative Iris Recognition using Improved Segmentation Methodology for Noisy Images

Сравнительный анализ методов некооперативного распознавания радужной оболочки глаза

Сравнительный анализ методов некооперативного распознавания радужной оболочки глаза

Noncooperative bovine iris recognition via SIFT

Iris biometry has been widely used to recognize an individual in a natural and intuitive way. Conventional iris recognition systems transfer iris images to rectangle using polar coordinate after accurate segmentation, and have performed very well on accurate data. However, bovine iris images are usually irregular with respect to inactive participant, and the conventional methods cannot achieve high accuracy and true rotation invariance. In this paper, a new scheme is proposed based on scale invariant feature transform (SIFT) and bag-of-features. Firstly, region-based active contour is used to detect the inner boundary. Secondly, SIFT method is applied to detect the keypoints in the iris image, and points located in pupil region are removed. Then, feature vocabulary is constructed, and histogram representation for each iris image is generated. Finally, histogram distance is adopted for the matching test. Experimental results are provided to show the effectiveness and potential of developed noncooperative iris recognition.

A new accurate noise-removing approach for non-cooperative iris recognition

The applications of biometric technology for automated personal identification become ubiquitous. Iris recognition is well known for high accuracy and reliability among the biometric traits. This paper presents an efficient noise-removing approach for non-cooperative iris recognition systems. Proposed method removed the noise factors including eyelids, eyelashes, reflections, out of framework, pupil and sclera. The novelty is to detect eyelashes and reflections through finding appropriate thresholds using a procedure called statistical decision making. The eyelids are detected using parabolic Hough transform in normalized iris image to increase computational speed. In addition, a coarse-to-fine strategy for accurate and fast iris localization is proposed. The Gabor-wavelet and a novel encoding strategy proposed in our previous work are also used here to generate the iris codes. We elaborate the principle of mask code generation to assign noisy bits in an iris code to exclude them in matching step. Experimental results on CASIA-IrisV3-Interval database show superiority of the proposed scheme among other state-of-the-art methods available in the literature.

Speed-up multi-stage non-cooperative iris recognition

Iris is tested to be the most accurate one. However, most existing methods are not designed for non-cooperative users and cannot work with off-angle or low quality iris images. In this paper, we propose a robust multi-stage feature extraction and matching approach for non-cooperative iris recognition. We developed the Speed Up Robust Feature (SURF)-like method to extract stable feature points, used Gabor Descriptor method for local feature description, and designed the multi-stage feature extraction and matching scheme to improve the recognition accuracy and speed. The experimental results show that the proposed method can not only improve the process speed but also achieve better recognition accuracy.

A fusion approach to unconstrained iris recognition

As biometrics has evolved, the iris has remained a preferred trait because its uniqueness, lifetime stability and regular shape contribute to good segmentation and recognition performance. However, commercially deployed systems are characterized by strong acquisition constraints based on active subject cooperation, which is not always achievable or even reasonable for extensive deployment in everyday scenarios. Research on new techniques has been focused on lowering these constraints without significantly impacting performance while increasing system usability, and new approaches have rapidly emerged. Here we propose a novel fusion of different recognition approaches and describe how it can contribute to more reliable noncooperative iris recognition by compensating for degraded images captured in less constrained acquisition setups and protocols under visible wavelengths and varying lighting conditions. The proposed method was tested at the NICE.II (Noisy Iris Challenge Evaluation – Part 2) contest, and its performance was corroborated by a third-place finish.

Accurate Iris Segmentation Method for Non-Cooperative Iris Recognition System

Problem statement: Iris segmentation is one of the most important ste ps in iris recognition system and determines the accuracy of matching. Most segmentation methods in the literature assumed that the inner and outer boundaries of the iris wer e circular. Hence, they focus on determining model parameters that best fit these hypotheses. This is a source of error, since the iris boundaries were n ot exactly circles. Approach: In this study we proposed an accurate iris segment ation method that employs Chan-Vese active contour method to extract the iris from his surrounding structures. Results: The proposed method was implemented and tested on the challenging UBIRIS database the results indicated the efficacy of the proposed meth od. Conclusion: The experimental results showed that the proposed method localized the iris area probabl y even when the eyelids occlude same part of iris.

Scale Invariant Gabor Descriptor-Based Noncooperative Iris Recognition

A new noncooperative iris recognitionmethod is proposed. In this method, the iris features are extracted using a Gabor descriptor. The feature extraction and comparison are scale, deformation, rotation, and contrast-invariant. It works with off-angle and lowresolution iris images. The Gabor wavelet is incorporated with scale-invariant feature transformation (SIFT) for feature extraction to better extract the iris features. Both the phase and magnitude of the Gabor wavelet outputs were used in a novel way for local feature point description. Two feature region maps were designed to locally and globally register the feature points and each subregion in the map is locally adjusted to the dilation/contraction/deformation. We also developed a video-based noncooperative iris recognition system by integrating video-based non-cooperative segmentation, segmentation evaluation, and score fusion units. The proposed method shows good performance for frontal and off-angle iris matching. Video-based recognition methods can improve non-cooperative iris recognition accuracy.

Video-Based Noncooperative Iris Image Segmentation

In this paper, we propose a video-based noncooperative iris image segmentation scheme that incorporates a quality filter to quickly eliminate images without an eye, employs a coarse-to-fine segmentation scheme to improve the overall efficiency, uses a direct least squares fitting of ellipses method to model the deformed pupil and limbic boundaries, and develops a window gradient-based method to remove noise in the iris region. A remote iris acquisition system is set up to collect noncooperative iris video images. An objective method is used to quantitatively evaluate the accuracy of the segmentation results. The experimental results demonstrate the effectiveness of this method. The proposed method would make noncooperative iris recognition or iris surveillance possible.

Robust iris recognition algorithm for non-cooperative environment

Non-cooperative iris recognition system results in blurred and noisy images that can degrade the performance of the system. This paper proposes a novel feature extraction algorithm for non-cooperative environment. The proposed iris subdivision method reduces the effect of noise due to non-cooperative behaviour in an authentication system. Optimal projection analysis is proposed to obtain the rotation invariant features from the directional subband coefficients of the normalised image block. A fusion algorithm combines the matching scores from individual subimages to reduce the false rejection rate. The performance of the proposed method is compared with other algorithms on UBIRIS iris image database.

Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition

This paper describes the winning algorithm we submitted to the recent NICE.I iris recognition contest. Efficient and robust segmentation of noisy iris images is one of the bottlenecks for non-cooperative iris recognition. To address this problem, a novel iris segmentation algorithm is proposed in this paper. After reflection removal, a clustering based coarse iris localization scheme is first performed to extract a rough position of the iris, as well as to identify non-iris regions such as eyelashes and eyebrows. A novel integrodifferential constellation is then constructed for the localization of pupillary and limbic boundaries, which not only accelerates the traditional integrodifferential operator but also enhances its global convergence. After that, a curvature model and a prediction model are learned to deal with eyelids and eyelashes, respectively. Extensive experiments on the challenging UBIRIS iris image databases demonstrate that encouraging accuracy is achieved by the proposed algorithm which is ranked the best performing algorithm in the recent open contest on iris recognition (the Noisy Iris Challenge Evaluation, NICE.I).

Region-based SIFT approach to iris recognition

Traditional iris recognition systems transfer iris images to polar (or log-polar) coordinates and have performed very well on data that tends to have a centered gaze. The patterns of an iris are part of a 3-D structure that is captured as a two-dimensional (2-D) image and cooperative iris recognition systems are capable of correctly matching these 2-D representations of iris features. However, when the gaze of an eye changes with respect to the camera lens, many times the size, shape, and detail of iris patterns will change as well and cannot be matched to enrolled images using traditional methods. Additionally, the transformation of off-angle eyes to polar coordinates becomes much more challenging and noncooperative iris algorithms will require a different approach. The direct application of the scale-invariant feature transform (SIFT) method would not work well for iris recognition because it does not take advantage of the characteristics of iris patterns. We propose the region-based SIFT approach to iris recognition. This new method does not require polar transformation, affine transformation or highly accurate segmentation to perform iris recognition and is scale invariant. This method was tested on the iris challenge evaluation (ICE), WVU and IUPUI noncooperative databases and results show that the method is capable of cooperative and noncooperative iris recognition.

Toward Noncooperative Iris Recognition: A Classification Approach Using Multiple Signatures

This paper focuses on noncooperative iris recognition, i.e., the capture of iris images at large distances, under less controlled lighting conditions, and without active participation of the subjects. This increases the probability of capturing very heterogeneous images (regarding focus, contrast, or brightness) and with several noise factors (iris obstructions and reflections). Current iris recognition systems are unable to deal with noisy data and substantially increase their error rates, especially the false rejections, in these conditions. We propose an iris classification method that divides the segmented and normalized iris image into six regions, makes an independent feature extraction and comparison for each region, and combines each of the dissimilarity values through a classification rule. Experiments show a substantial decrease, higher than 40 percent, of the false rejection rates in the recognition of noisy iris images.