Performance Of Iris Recognition System Research Articles

Synthetic Iris Images: A Comparative Analysis between Cartesian and Polar Representation.

In recent years, the advancement of generative techniques, particularly generative adversarial networks (GANs), has opened new possibilities for generating synthetic biometric data from different modalities, including-among others-images of irises, fingerprints, or faces in different representations. This study presents the process of generating synthetic images of human irises, using the recent StyleGAN3 model. The novelty presented in this work consists in producing generated content in both Cartesian and polar coordinate representations, typically used in iris recognition pipelines, such as the foundational work proposed by John Daugman, but hitherto not used in generative AI experiments. The main objective of this study was to conduct a qualitative analysis of the synthetic samples and evaluate the iris texture density and suitability for meaningful feature extraction. During this study, a total of 1327 unique irises were generated, and experimental results carried out using the well-known OSIRIS open-source iris recognition software and the equivalent software, wordlcoin-openiris, newly published at the end of 2023 to prove that (1) no "identity leak" from the training set was observed, and (2) the generated irises had enough unique textural information to be successfully differentiated between both themselves and between them and real, authentic iris samples. The results of our research demonstrate the promising potential of synthetic iris data generation as a valuable tool for augmenting training datasets and improving the overall performance of iris recognition systems. By exploring the synthetic data in both Cartesian and polar representations, we aim to understand the benefits and limitations of each approach and their implications for biometric applications. The findings suggest that synthetic iris data can significantly contribute to the advancement of iris recognition technology, enhancing its accuracy and robustness in real-world scenarios by greatly augmenting the possibilities to gather large and diversified training datasets.

An efficient interior and exterior boundary detection-based two level iris segmentation

Iris recognition system's performance relies upon the method of segmentation of iris from the eye image. The segmentation process of iris still stumbles upon with few tricky challenges, particularly in separating the iris from eye image. The prevailing eyelids and eyelashes in the image leads to the lessening of the accuracy. A two-level segmentation' methodology was proposed. Initially, the iris image was converted to greyscale image and normalised. The adaptive median filter (AMF) was utilised to remove the noise. The noise removable image was segmented using two-level segmentation method. In interior boundary segmentation section, the image was segmented utilising some methods like Gaussian pyramid, anisotropic diffusion, thresholding, centroid computing, polar transform, and radius computing. Exterior boundary segmentation section performs zigzag collarette process. Finally, the IBS was subtracted from EBS; to give the segmented iris. Experimental results when compared with ACWOE and K-means demonstrated the superiority of the proposed technique.

An efficient interior and exterior boundary detection-based two level iris segmentation

Iris recognition system's performance relies upon the method of segmentation of iris from the eye image. The segmentation process of iris still stumbles upon with few tricky challenges, particularly in separating the iris from eye image. The prevailing eyelids and eyelashes in the image leads to the lessening of the accuracy. A two-level segmentation' methodology was proposed. Initially, the iris image was converted to greyscale image and normalised. The adaptive median filter (AMF) was utilised to remove the noise. The noise removable image was segmented using two-level segmentation method. In interior boundary segmentation section, the image was segmented utilising some methods like Gaussian pyramid, anisotropic diffusion, thresholding, centroid computing, polar transform, and radius computing. Exterior boundary segmentation section performs zigzag collarette process. Finally, the IBS was subtracted from EBS; to give the segmented iris. Experimental results when compared with ACWOE and K-means demonstrated the superiority of the proposed technique.

Improving identification performance in iris recognition systems through combined feature extraction based on binary genetics

The process of iris recognition is composed of several stages. The most important stage is feature extraction. The majority of systems use a single method of feature extraction. We used a binary genetic algorithm with a new fitness criterion to find an efficient combination of feature extraction methods with the aim of improving the performance of iris recognition systems. The proposed method makes use of a large number of filters and transforms which are widely used to extract iris features. The best combination of these techniques is found over iterations of the proposed algorithm. The final combination, which is argued to be the most efficient method of feature extraction, consists of a number of wavelet transforms, Gabor filter, and Fourier transform. ROC graphs are used to show the experimental superiority of the performance of this method over methods that use a single filter. According to our findings, the proposed method shows better performance in most situations. An FAR value of 0 and an FRR value of 0.092 was obtained by this method.

Iris Segmentation

The quality of eye image data become degraded particularly when the image is taken in the non-cooperative acquisition environment such as under visible wavelength illumination. Consequently, this environmental condition may lead to noisy eye images, incorrect localization of limbic and pupillary boundaries and eventually degrade the performance of iris recognition system. Hence, this study has compared several segmentation methods to address the abovementioned issues. The results show that Circular Hough transform method is the best segmentation method with the best overall accuracy, error rate and decidability index that more tolerant to ‘noise’ such as reflection.

Improving Unconstrained Iris Recognition Performance via Domain Adaptation Metric Learning Method

To improve unconstrained iris recognition system performance in different environments, a performance improvement method of unconstrained iris recognition based on domain adaptation metric learning is proposed. A kernel matrix is calculated as the solution of domain adaptation metric learning. The known Hamming distance computing by intra-class and inter-class is used as the optimization learning constraints in the process of iris recognition. An optimal Mahalanobis matrix is computed for certain cross-environment system, then distance between two iris samples is redefined. The experimental results indicate that the proposed method can increase the accuracy of the unconstrained iris recognition in different circumstances, improving the classification ability of iris recognition system.

An Image Based Approach of IRIS Recognition for Person Identification using Segmentation Algorithm

A biometric system gives automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition system is regarded as the most reliable and accurate biometric identification system available. Also, Iris recognition is considered to be the most reliable and accurate biometric identification system present. Iris recognition system captures an image of an individual’s eye, the iris in the image is then meant for the segmentation and normalization for extracting its features. The performance of iris recognition systems mainly depends on the segmentation. Segmentation is used for the localization of the correct iris region in the particular region of an eye and it should be done accurately and correctly to remove the eyelids, eyelashes, reection and pupil noises present in iris region. Iris images are selected from the Database, then the iris and pupil boundary is detected from rest of the eye image,by removing the noises. The segmented iris region was normalized to compress the dimensional inconsistencies between two iris regions by using Then the features of the iris were encoded by convolving the normalized iris. The Hamming distance was chosen as a matching metric parameter, which gave the measure of how many bits disagreed between the templates of the iris .

Iris Image Enhancement for the Recognition of Non-ideal Iris Images

Iris recognition for biometric personnel identification has gained much interest owing to the increasing concern with security today. The image quality plays a major role in the performance of iris recognition systems. When capturing an iris image under uncontrolled conditions and dealing with non-cooperative people, the chance of getting non-ideal images is very high owing to poor focus, off-angle, noise, motion blur, occlusion of eyelashes and eyelids, and wearing glasses. In order to improve the accuracy of iris recognition while dealing with non-ideal iris images, we propose a novel algorithm that improves the quality of degraded iris images. First, the iris image is localized properly to obtain accurate iris boundary detection, and then the iris image is normalized to obtain a fixed size. Second, the valid region (iris region) is extracted from the segmented iris image to obtain only the iris region. Third, to get a well-distributed texture image, bilinear interpolation is used on the segmented valid iris gray image. Using contrast-limited adaptive histogram equalization (CLAHE) enhances the low contrast of the resulting interpolated image. The results of CLAHE are further improved by stretching the maximum and minimum values to 0–255 by using histogram-stretching technique. The gray texture information is extracted by 1D Gabor filters while the Hamming distance technique is chosen as a metric for recognition. The NICE-II training dataset taken from UBRIS.v2 was used for the experiment. Results of the proposed method outperformed other methods in terms of equal error rate (EER).

Iris Segmentation for Non-ideal Images

Iris recognition has been regarded as one of the most reliable biometric systems over the past years. Previous studies have shown that the performance of iris recognition systems highly dependent on the performance of their segmentation algorithms. Iris segmentation is the process to isolate the iris region from the surrounded structures of the eye image. However, several iris segmentation algorithms have been developed in the literature, but their segmentation and recognition accuracies drastically degrade with non-ideal iris images acquired in less constrained conditions. Thus, it is crucial to develop a new iris segmentation method to improve iris recognition using non-ideal images. Hence, the objective of this paper is an iris segmentation method on the basis of optimization to isolate the iris region from non-ideal iris images such those affected by reflections, blurred boundaries, eyelids occlusion, and gaze-deviation. Experimental results on the off axis/angle West Virginia University (WVU) iris database demonstrated the superiority of the developed method over state-of-the-art iris segmentation methods considered in this paper. The performance of an iris recognition algorithm based on the developed iris segmentation method was observed to be improved.

Automated Iris Recognition System: An Overview

Iris texture pattern can be used for biometric verification and identification of a person from a large dataset. Iris recognition is used in several fields, like border, security prone, industrial and medical institutes etc. Due to its high correctness and uniqueness, it is used in several fields of access control and border area security. The demand for iris recognition is increasing continuously due to its reliability, accuracy and uniqueness. To improve the overall recognition rate and performance of iris recognition system, the researchers have to work in different aspects like unconstrained environment, noisy images as well as blurred images. This paper overviews the different steps involve into iris recognition system as well as various methodology presents in the iris recognition system.

Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion

In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.

Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion

For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks' information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system.

Fast and Accurate Pupil Isolation Based on Morphology and Active Contour

The performance of iris recognition systems is significantly affected by the segmentation accuracy, especially in non-ideal iris images. This paper proposes a fast and accurate method to segment the pupil regardless of the shape using a combination of morphological operations and snake active contour. The morphological operations are used to determine a rough boundary of the pupil region while the active contour is used to find the precise boundary. The proposed scheme is robust in finding the exact pupil boundary of non-ideal iris image. Experimental results on the CASIA version 4.0 iris databases have indicated a high level of accuracy using the proposed technique.

VASIR: An Open-Source Research Platform for Advanced Iris Recognition Technologies.

The performance of iris recognition systems is frequently affected by input image quality, which in turn is vulnerable to less-than-optimal conditions due to illuminations, environments, and subject characteristics (e.g., distance, movement, face/body visibility, blinking, etc.). VASIR (Video-based Automatic System for Iris Recognition) is a state-of-the-art NIST-developed iris recognition software platform designed to systematically address these vulnerabilities. We developed VASIR as a research tool that will not only provide a reference (to assess the relative performance of alternative algorithms) for the biometrics community, but will also advance (via this new emerging iris recognition paradigm) NIST’s measurement mission. VASIR is designed to accommodate both ideal (e.g., classical still images) and less-than-ideal images (e.g., face-visible videos). VASIR has three primary modules: 1) Image Acquisition 2) Video Processing, and 3) Iris Recognition. Each module consists of several sub-components that have been optimized by use of rigorous orthogonal experiment design and analysis techniques. We evaluated VASIR performance using the MBGC (Multiple Biometric Grand Challenge) NIR (Near-Infrared) face-visible video dataset and the ICE (Iris Challenge Evaluation) 2005 still-based dataset. The results showed that even though VASIR was primarily developed and optimized for the less-constrained video case, it still achieved high verification rates for the traditional still-image case. For this reason, VASIR may be used as an effective baseline for the biometrics community to evaluate their algorithm performance, and thus serves as a valuable research platform.

A New Snake Model for Pupil Localization Using Orthogonal Polynomials Transform

 Abstract—In this paper a new approach for representing and evolving deformable contour or snake model to accurately detect pupil boundary for improving the performance of iris recognition systems is proposed. The proposed model extracts the boundary with computationally efficient Laplacian of Guassian (LoG) mask. The LoG mask is obtained from the set of polynomial basis operators derived from Orthogonal Polynomials Transform. Two types of controlling force models, introduced as internal and external forces are designed to properly activate the contour and locate it over the pupil boundary. The internal forces are designed to smooth the contour as well as to keep it close to a boundary by pushing the contour vertices towards the boundary of interest. The external forces are responsible for pulling the contour vertices towards the pupil boundary and the contour is deformed into a new shape in response to the effective force. A revised contour estimate of the selected boundary based on the execution of the contour tracing algorithm is generated. The results are highly encouraging to capture the contour of non-circular shaped pupil of iris. Experimental results on the CASIA v1.0 iris database demonstrate that the proposed snake model outperforms in both accuracy and speed.

Iris Segmentation using Geodesic Active Contour for Improved Texture Extraction in Recognition

identification/verification of a person through biometrics has been getting extensive attention due to an increasing importance of security. The most popular biometric authentication scheme employed for the last few years is Iris Recognition. The performance of iris recognition system highly depends on segmentation. For instance, even an effective feature extraction method would not be able to obtain useful information from an iris image that is not segmented accurately. The iris proposed recognition module consists of the preprocessing system, segmentation, feature extraction and recognition. Mainly it focuses on image segmentation using Geodesic Active Contours and comparison with traditional methods of segmentation. As active contours can 1) assume any shape and 2) segment multiple objects at the same time, they lessen some of the concerns related with conventional iris segmentation models. The iris texture is extracted in an iterative fashion by considering both local and global properties of the image. The matching accuracy of an iris recognition system is observed to improve upon application of the proposed segmentation algorithm. Experimental results on the CASIA (Institute of Automation, Chinese Academy of Sciences) Interval version3 iris databases implemented in MATLAB shows the efficiency of the proposed technique application.

Novel Approaches to Improve Robustness, Accuracy and Rapidity of Iris Recognition Systems

Iris authentication is one of the most successful applications in video analysis and image processing. In this paper, several novel approaches are proposed to improve the overall performance of iris recognition systems. First, this paper proposes a new eyelash detection algorithm based on directional filters, which achieves a low rate of eyelash misclassification. Second, a multiscale and multidirection data fusion method is introduced to reduce the edge effect of wavelet transformation produced by complex segmentation algorithms. Finally, an iris indexing method on the basis of corner detection is presented to accelerate exhausted the 1: N search in a huge iris database. The performance evaluations are carried out on two popular iris databases, and the test results are experimentally more robust and accurate with less elapsed time compared with most existing methods.

An efficient color and texture based iris image retrieval technique

This paper proposes a hierarchical approach to retrieve an iris image efficiently from for a large iris database. This approach is a combination of both iris color and texture. Iris color is used for indexing and texture is used for retrieval of iris images from the indexed iris database. An index which is determined from the iris color is used to filter out the images that are not similar to the query image in color. Further, iris texture features of those filtered images, are used to determine the images which are similar to the query image. The iris color information helps to design an efficient indexing scheme based on color indices. The color indices are computed by averaging the intensity values of all red and blue color pixels. Kd-tree is used for real-time indexing based on color indices. The iris texture features are obtained through Speeded Up Robust Features (SURF) algorithm. These features are used to get the query’s corresponding image at the top best match. The performance of the proposed indexing scheme is compared with two well known iris indexing schemes ( Mehrotra, Majhi, & Gupta, 2010; Puhan & Sudha, 2008) on UPOL ( Dobeš, Machala, Tichavský, & Pospı´šil, 2004) and UBIRIS ( Proencca & Alexandre, 2005) iris databases. It is observed that combination of iris color and texture improves the performance of iris recognition system.

Transforming Traditional Iris Recognition Systems to Work in Nonideal Situations

Under a nonideal situation, the image quality may vary. As a result, the traditional iris recognition systems would not work well. However, these kinds of iris recognition systems have been widely deployed in law enforcement and homeland security. It will be desirable to transform the traditional systems to perform in nonideal situations without a costly update. In this paper, we propose a method that upgrades the traditional iris recognition system to work on nonideal situations. The proposed method takes into consideration not only the effect of image quality but also the segmentation accuracy. It employs video-based image-processing techniques to quickly identify and eliminate the bad quality images from iris videos for further processing. The proposed method is tested on public databases using in-house recognition algorithms and also evaluated using a commercialized system. The research results show that the proposed methods can be used to improve the performance of iris recognition systems in a nonideal situation.

Effects of image compression on iris recognition system performance

The human iris is perhaps the most accurate biometric for use in identification. Commercial iris recognition systems currently can be found in several types of settings where a person’s true identity is required: to allow passengers in some airports to be rapidly processed through security; for access to secure areas; and for secure access to computer networks. The growing employment of iris recognition systems and the associated research to develop new algorithms will require large databases of iris images. If the required storage space is not adequate for these databases, image compression is an alternative. Compression allows a reduction in the storage space needed to store these iris images. This may, however, come at a cost: some amount of information may be lost in the process. We investigate the effects of image compression on the performance of an iris recognition system. Compression is performed using JPEG-2000 and JPEG, and the iris recognition algorithm used is an implementation of the Daugman algorithm. The imagery used includes both the CASIA iris database as well as the iris database collected by the University of Bath. Results demonstrate that compression up to 50:1 can be used with minimal effects on recognition.