Iris Segmentation Research Articles

A Novel Edge-Map Creation Approach for Highly Accurate Pupil Localization in Unconstrained Infrared Iris Images

Iris segmentation in the iris recognition systems is a challenging task under noncooperative environments. The iris segmentation is a process of detecting the pupil, iris’s outer boundary, and eyelids in the iris image. In this paper, we propose a pupil localization method for locating the pupils in the non-close-up and frontal-view iris images that are captured under near-infrared (NIR) illuminations and contain the noise, such as specular and lighting reflection spots, eyeglasses, nonuniform illumination, low contrast, and occlusions by the eyelids, eyelashes, and eyebrow hair. In the proposed method, first, a novel edge-map is created from the iris image, which is based on combining the conventional thresholding and edge detection based segmentation techniques, and then, the general circular Hough transform (CHT) is used to find the pupil circle parameters in the edge-map. Our main contribution in this research is a novel edge-map creation technique, which reduces the false edges drastically in the edge-map of the iris image and makes the pupil localization in the noisy NIR images more accurate, fast, robust, and simple. The proposed method was tested with three iris databases: CASIA-Iris-Thousand (version 4.0), CASIA-Iris-Lamp (version 3.0), and MMU (version 2.0). The average accuracy of the proposed method is 99.72% and average time cost per image is 0.727 sec.

A brief survey on multispectral iris recognition

In this survey, we report the current state of the art of multispectral iris recognition methods. We discuss the existing multispectral iris recognition schemes in the contexts of image acquisition, iris segmentation, texture analysis, matching, and performance evaluation. Other important research includes experimental evaluations, image databases, applications and systems, spectral ranges, and multispectral fusion that may affect the multispectral iris recognition.

Analysis of Iris Segmentation using Circular Hough Transform and Daughman's Method

Analysis of Iris Segmentation using Circular Hough Transform and Daughman's Method

High contrast electrochromic iris.

We present a non-mechanical microiris based on two complementary electrochromic (EC) materials, namely viologens and phenozines, with an almost neutral spectral behavior. Measurements concerning the spectral light transmission, modulation transfer function, and response time validate that the optical performance of the EC-iris is comparable to that of a classical blade iris. The time constant is limited due to diffusive mass transport of the molecules, but can be reduced by a short voltage pulse. The current controlled transmission of the EC-material renders the individual control of each iris segment without crosstalk possible, allowing its usage as tunable spatial filter.

Iris Recognition Using the Nonsubsampled Contourlet Transform

Biometric information is widely used in user identification system. Because of the unique and invariant properties of the iris through a lifetime, iris recognition is one of the most stable and reliable means in biometric identification. Extracting distinguishable iris features for iris recognition is very important. In this paper, for capturing effective texture features that represent the complex directional structures of an iris image, a new iris recognition method using the nonsubsampled contourlet transform (NSCT) features is proposed. With the shift-invariance, multiscale, and multidirection properties, significant NSCT coefficient features along the radial and angular directions in an iris image can be represented efficiently. Iris segmentation and normalization are considered at first as pre-processing. The modified normalized iris image is obtained from the normalized iris regions for extracting the robust iris features, and then is filtered with the NSCT to obtain the distinct coefficient features in each directional subband. Next, using the NSCT coefficients in each subband, an iris code vector is constructed for iris matching. Comparison of experimental results of the proposed and existing methods with three databases show the effectiveness of the proposed NSCT feature-based iris recognition algorithm, in terms of the three performance measures.

Effect of Eyelid and Eyelash Occlusions on a Practical Iris Recognition System: Analysis and Solution

One of the crucial and inherent issues in a practical iris recognition system is the occlusion that happens due to eyelids and eyelashes. This occlusion increases the complexity and degrades the performance of matching and feature extraction processes. Generally, two types of approaches have been proposed to solve this issue. The first approach requires generating an iris mask that indicates which part of the iris is useful and which others are occluded. However, in the second approach, a fixed region of interest (ROI) within the iris area is selected to avoid the regions of occlusion. In this paper, we experimentally study both approaches but due to the latter characteristic, which is its ability to simplify the matching and feature extraction processes, it has been adopted in our techniques used, specifically for iris segmentation, iris normalization, and feature extraction. Accordingly, for matching and feature extraction, the lower side of the pupillary region (i.e. the innermost 25% of the lower half of the iris ring) is found to be the best ROI. This small area of iris is almost free of eyelids and eyelashes and it contains abundant texture information. Interestingly, this selection of small area helps us in proposing a simple yet efficient technique for feature extraction, called mean-based feature extraction technique (MB-FET). This technique is based on analyzing the local intensity variations. The proposed technique achieves a lower processing burden than other traditional methods such as Fourier or wavelet decompositions (e.g. Gabor wavelet). In most traditional techniques, many parameters (e.g. five parameters for 2D-Gabor filter) must be optimally determined in advance to achieve an accurate feature extraction process. Unfortunately, these parameters may not match various variations in image capturing conditions (e.g. variations in illumination due to change in image capturing distance). Moreover, the basic functions of the traditional methods are fixed in advance (off-line) and do not necessarily match the texture of all irises in the database. However, for our proposed technique MB-FET, there is no need to determine in advance any parameter or basic function. MB-FET dynamically adapts its parameter (only one parameter) with intensity variations. The proposed technique generates a binary iris code, hence a simple and fast matching process is done using the Hamming distance. The experimental results using the CASIA iris database show that the proposed technique achieves promising results for a robust and reliable iris recognition.

Novel Segmentation of Iris Images for Biometric Authentication Using Multi Feature Volumetric Measure

The aim of the research is to improve the efficiency of biometric authentication using different features of iris image. The biometric authentication and verification has become more popular where the authentication is more essential in many organizations. There are many approaches has been discussed to segment the iris image and to perform verification but suffers with the problem of accuracy in feature extraction and segmentation. To resolve such problems and to improve the efficiency of iris segmentation and recognition, we propose a novel segmentation algorithm which uses multi level filter which removes the eyelids and eyelash features and performs the edge detection to identify the inner and outer eye regions. Once the regions has been identified then, we compute various measures like the size of inner and outer eyes and extract the features of both and convert them in to feature vectors. The generated feature vectors are used to perform classification in biometric authentication approach. The multi feature volumetric measure is computed on the feature vector of each eye image where the feature vector has various features like the size of both inner and outer eyes, width and height, the original binary features, the number of binary ones and the number of pixels damaged by any form of disease and so on. Based on these features the MFVM is computed to classify the iris image towards a big data set of biometric features to perform authentication. The proposed method has improved the efficiency of iris segmentation and improved the efficiency of iris recognition based biometric authentication. Also the approach has reduced the time complexity and improved the efficiency also.

Frame detection using gradients fuzzy logic and morphological processing for distant color eye images in an intelligent iris recognition system

The capture of an eye image with the occlusion of spectacles in a non-cooperative environment compromises the accuracy in identifying a person in an iris recognition system. This is due to the obstruction of the iris by the frame which tends to produce an incorrect estimation of the initial center of the iris and the pupil during the iris segmentation process. In addition, it also causes incorrect localization of the upper eyelid during the process of iris segmentation and sometimes, the edges of the frame are wrongly identified as the edges of the upper eyelid. A frame detection method which involves the combination of two gradients, namely the Sobel operator and high pass filter, followed by fuzzy logic and the dilation operation of morphological processing is proposed to identify the frame on the basis of different frame factors in the capture of a distant eye image. In addition, a different color space is applied and only a single channel is used for the process of frame detection. The proposed frame detection method provides the highest frame detection rate compared to the other methods, with a detection rate of more than 80.0%. For the accuracy of the iris localization, upper eyelid localization and iris recognition system, the proposed method gives more than 96.5% accuracy compared to the other methods. The index of decidability showed that the proposed method gives more than 2.35 index compared to the existing methods.

WIRE: Watershed based iris recognition

A Watershed transform based Iris REcognition system (WIRE) for noisy images acquired in visible wavelength is presented. Key points of the system are: the color/illumination correction pre-processing step, which is crucial for darkly pigmented irises whose albedo would be dominated by corneal specular reflections; the criteria used for the binarization of the watershed transform, leading to a preliminary segmentation which is refined by taking into account the watershed regions at least partially included in the best iris fitting circle; the introduction of a new cost function to score the circles detected as potentially delimiting limbus and pupil. The advantage offered by the high precision of WIRE in iris segmentation has a positive impact as regards the iris code, which results to be more accurately computed, so that the performance of iris recognition is also improved. To assess the performance of WIRE and to compare it with the performance of other available methods, two well known databases have been used, specifically UBIRIS version 1 session 2 and the subset of UBIRIS version 2 that has been used as training set for the international challenge NICE II.

Robust iris segmentation algorithm based on self-adaptive Chan–Vese level set model

Iris segmentation is the first and critical step in an iris recognition system. A robust iris segmentation algorithm based on the self-adaptive Chan and Vese (SACV) level set model is proposed. First, the process of constructing the SACV model based on analyses of the corresponding requirements for the CV model is described when it is applied on iris segmentation. Second, the coarse segmentation of pupil and iris, which are localized based on image pixel gray information, is used as the initial contour of the SACV model. Third, the interference factors, such as eyelashes and eyelids, are detected and evaluated simultaneously to generate the interference degree and then the related parameter of SACV is set according to the interference degree. Finally, SACV is used to conduct the final fine segmentation of the pupil and iris. Experiments on four public iris image databases (e.g., CASIA-V1, CASIA-V3 Interval, CASIA-V3 Lamp, and MMU-V1) demonstrate the segmentation accuracy performance of the proposed algorithm, and at the same time, the proposed algorithm also displays robust performance in noisy situations, such as Gaussian, Poisson, salt-and-pepper, and speckle noises. Moreover, comparisons with the well-known methods further show that our algorithm can segment iris images more accurately.

Iris recognition using multiscale morphologic features

A new set of features for personal verification and identification based on iris image is proposed in this paper. The method consists of three major components: image pre-processing, feature extraction and classification. During image pre-processing, the iris segmentation is carried out using Restricted Circular Hough transformation (RCHT). Then only two disjoint quarters of the segmented iris pattern are normalized which is used to extract features for classification purposes. Here, method for feature extraction from iris pattern is based on multiscale morphologic operator. In this approach, the iris features are represented by the sum of dissimilarity residues obtained by applying morphologic top-hat transform. For classification purposes the multi-class problems is transformed to two-class problem using dichotomy method. The performance of the proposed system is tested on four benchmark iris databases UPOL, MMU1, IITD, and UBIRIS and is compared with well known existing methods.

BIOMETRIC PERSONAL IDENTIFICATION ON 2D WAVELET TRANSFORM AND CHI-SQUARED MODEL

Iris recognition system consists of image acquisition, iris preprocessing, iris segmentation and feature extraction with comparism (matching) stages. The biometric based personal identification using iris requires accurate iris segmentation for successful identification or recognition. Recently, several researchers have implemented various methods for segmentation of boundaries which will require a modification of some of the existing segmentation algorithms for their proper recognition. Therefore, this research presents a 2D Wavelet Transform and Chi-squared model for iris features extraction and recognition. Circular Hough Transform was used for the segmentation of the iris image. The system localizes the circular iris and pupil region and removes the occluding eyelids and eyelashes. The extracted iris region is normalized using Daugman’s rubber sheet model into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data (iris signature) from the 2D wavelet transform data is extracted, forming the biometric template. The chi-squared distance is employed for classification of iris templates and recognition. Implementing this model can enhance identification. Based on the designed system, an FAR (False Acceptances ratio) of 0.00 and an FRR (False Rejection Ratio) of 0.896 was achieved.

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.

Research on Iris Localization Algorithm based on the Active Contour Model

An automatic and robust iris segmentation algorithm will be employed when iris recognition is used to identify human efficiently. Many studies have shown that iris localization is one of the most important parts of the iris recognition system. Most current studies are based on the assumption that iris edge is round, and the gray information of eye image is used for iris segmentation, which requires very high quality of image. Based on the active contour model, an algorithm for iris location was proposed. First, a plurality of pupil edge points was located as the initial contour point set. Then, the real contour evolution of pupil was approximated by the approximation of energy function. Finally, the accurate iris edge was located by daugman’s circular gradient operator. Based on CASIA (V2.0) iris database, experiment results show that the location effect of the proposed algorithm is more superior than that of the classical calculus method and hough transform method.

Performance Evaluation of Proposed Segmentation Framework with Existing Techniques for Noisy Iris Images

different iris segmentation techniques continue to appear, there has been a lack of recognition accuracy of existing methods with noisy iris dataset. To handle iris images (captured on less constrained conditions) with some types of noise (iris obstructions and specular reflection), the authors shows the results of performance evaluation of their proposed iris segmentation technique over existing techniques. The performance of a proposed iris segmentation technique is evaluated based on the accuracy and time. To evaluate the performance, the authors use the most important points to compare their proposed technique with others, which is Equal Error Rate (EER). The system is implemented and tested using MATLAB Version 7.5.0.342 (R2007b) software. The environment where the experiments are performed in is Compaq PC, Core 2 Due Intel Pentium Processor (2.00 GHz), with 1GB RAM and Windows 7 operating system, a dataset of UBIRIS v1, UBIRIS v2 and CASIA-IrisV4 databases samples of iris data with different contrast quality.

Improving colour iris segmentation using a model selection technique

In this paper, we propose a novel method to improve the reliability and accuracy of colour iris segmentation for captures both from static and mobile devices. Our method is a fusion strategy based on selection among the segmentation outcomes of different segmentation methods or models. First, we present and analyse an iris segmentation framework which uses three different models to show that improvements can be obtained by selection among the outcomes generated by the three models. Then, we introduce a model selection method which defines the optimal segmentation based on a ring-shaped region around the outer segmentation boundary identified by each model. We use the histogram of oriented gradients (HOG) as features extracted from the ring-shaped region, and train a SVM-based classifier which provides the selection decision. Experiments on colour iris datasets, captured by mobile devices and static camera, show that the proposed method achieves an improved performance compared to the individual iris segmentation models and existing algorithms.

Robust Detection of Textured Contact Lenses in Iris Recognition Using BSIF

This paper considers three issues that arise in creating an algorithm for the robust detection of textured contact lenses in iris recognition images. The first issue is whether the accurate segmentation of the iris region is required in order to achieve the accurate detection of textured contact lenses. Our experimental results suggest that accurate iris segmentation is not required. The second issue is whether an algorithm trained on the images acquired from one sensor will well generalize to the images acquired from a different sensor. Our results suggest that using a novel iris sensor can significantly degrade the correct classification rate of a detection algorithm trained with the images from a different sensor. The third issue is how well a detector generalizes to a brand of textured contact lenses, not seen in the training data. This paper shows that a novel textured lens type may have a significant impact on the performance of textured lens detection.

Artificial bees for multilevel thresholding of iris images

In this paper, a multilevel thresholding based on Artificial Bee Colony metaheuristic is proposed as a pre-segmentation step in the iris detection process. Multilevel thresholding helps in the unification of the iris region and the attenuation of the noise outside and inside the iris region that mainly affects the process of iris segmentation. Since it depends on exhaustive search, multilevel thresholding is time consuming especially if the number of thresholds is not restricted, though it yields convenient results. Two variants of Artificial Bee Colony (ABC) metaheuristic, namely, the basic ABC and the G-best guided ABC in addition to Cuckoo Search (CS) and Particle Swarm Optimisation (PSO) metaheuristics are then used to look for the best thresholds distribution delimiting the components of the iris image for improving the iris detection results. To test our approach, we have opted for the Integro-differential Operator of Daughman and the Masek method for the principal segmentation process on both the standard databases CASIA and UBIRIS. As a result, qualitatively the segmented iris images are enhanced; numerically the iris detection rate improved and became more accurate.

Elastic strips normalisation model for higher iris recognition performance

Iris recognition is among the best biometric systems. Characterised by the iris's uniqueness, universality, distinctiveness, permanence and collectability, the iris recognition system achieves high performance and real time response. In this study, the authors propose an improved iris normalisation model applied after a precise iris segmentation process. The normalisation model defines a new reference space for iris features. It normalises the iris using radial strips with a shape that changes between the pupil's boundary and the circular approximation of the iris's outer boundary. Moreover, the effect of the centres of the normalisation strips is evaluated by assessing the recognition performance when comparing three different centres configurations. The approach is tested on 2491 images from the CASIA V3 database. The system's performance is measured at the matching stage. Higher decidability and recognition accuracy at equal error rate is obtained. Detection error tradeoff curves are estimated by using the proposed model and compared with Daugman's reference system for assessing performance improvement.

Segmenting iris images in the visible spectrum with applications in mobile biometrics

The widespread use of mobile devices with Internet connectivity has resulted in the storage and transmission of sensitive data. This has heightened the need to perform reliable user authentication on mobile devices in order to prevent an adversary from accessing such data. Biometrics, the science of recognizing individuals based on their biological and behavioral traits, has the potential to be leveraged for this purpose. In this work, we briefly discuss the suitability of using the iris texture for biometric recognition in mobile devices. One of the critical components of an iris recognition system is the segmentation module which separates the iris from other ocular attributes. Since current mobile devices acquire color images of an object, we conduct a literature review for performing automated iris segmentation in the visible spectrum. The goal is to convey the possibility of successfully incorporating iris recognition in mobile devices.