Iris Localization Research Articles

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor.

Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies.

Iris localization using rough entropy and CSA: A soft computing approach

Identification of a person depends on the proper extraction of the iris region. Segmentation, being the first step in iris analysis, constitutes the most important phase in iris localization. Images are often captured in non-ideal conditions, and are incomplete with different kinds of associated uncertainties. Therefore, iris segmentation assumes paramount importance towards its subsequent localization and analysis. A novel soft-computing approach is proposed for the segmentation of iris based on rough entropy, with localization using circular sector analysis (CSA); thereby minimizing uncertainties.We compare the performance of this algorithm with that by the circular Hough transform, which is state-of-the-art in approximating the iris region although being computationally intensive. The proposed rough entropy based segmentation, followed by CSA for localization of iris, is found to perform more efficiently and accurately in comparison to the state-of-the-art methodologies.

Supervised Growing Approach for Region of Interest Detection in Iris Localisation

Supervised Growing Approach for Region of Interest Detection in Iris Localisation

IRIS TECHNOLOGY: A REVIEW ON IRIS BASED BIOMETRIC SYSTEMS FOR UNIQUE HUMAN IDENTIFICATION

Biometric features are widely used in real time applications for unique human identification. Iris is one of the physiological biometric features which are regarded as highly reliable in biometric identification systems. Often iris is combined with other biometric features for robust biometric systems. It is also observed that biometrics is combined with cryptography for stronger security mechanisms. Since iris is unique for all individuals across the globe, many researchers focused on using iris or along with other biometrics for security with great precision. Multimodal biometric systems came into existence for better accuracy in human authentication. However, iris is considered to be most discriminatory of facial biometrics. Study of iris based human identification in ideal and non-cooperative environments can provide great insights which can help researchers and organizations that depend on iris-based biometric systems. The technical knowhow of iris strengths and weaknesses can be great advantage. This is more important in the wake of widespread use of smart devices which are vulnerable to attacks. This paper throws light into various iris-based biometric systems, issues with iris in the context of texture comparison, cancellable biometrics, iris in multi-model biometric systems, iris localization issues, challenging scenarios pertaining to accurate iris recognition and so on.

Quick Bootstrapping of a Personalized Gaze Model from Real-Use Interactions

Understanding human visual attention is essential for understanding human cognition, which in turn benefits human--computer interaction. Recent work has demonstrated a Personalized, Auto-Calibrating Eye-tracking (PACE) system, which makes it possible to achieve accurate gaze estimation using only an off-the-shelf webcam by identifying and collecting data implicitly from user interaction events. However, this method is constrained by the need for large amounts of well-annotated data. We thus present fast-PACE, an adaptation to PACE that exploits knowledge from existing data from different users to accelerate the learning speed of the personalized model. The result is an adaptive, data-driven approach that continuously “learns” its user and recalibrates, adapts, and improves with additional usage by a user. Experimental evaluations of fast-PACE demonstrate its competitive accuracy in iris localization, validity of alignment identification between gaze and interactions, and effectiveness of gaze transfer. In general, fast-PACE achieves an initial visual error of 3.98 degrees and then steadily improves to 2.52 degrees given incremental interaction-informed data. Our performance is comparable to state-of-the-art, but without the need for explicit training or calibration. Our technique addresses the data quality and quantity problems. It therefore has the potential to enable comprehensive gaze-aware applications in the wild.

Modeling and prototyping of biometric systems using dataflow programming

The development of biometric systems is one of the labor-intensive processes. Therefore, the creation and analysis of approaches and techniques is an urgent task at present. This article presents a technique of modeling and prototyping biometric systems based on dataflow programming. The technique includes three main stages: the development of functional blocks, the creation of a dataflow graph and the generation of a prototype. A specially developed software modeling environment that implements this technique is described. As an example of the use of this technique, an example of the implementation of the iris localization subsystem is demonstrated. A variant of modification of dataflow programming is suggested to solve the problem related to the undefined order of block activation. The main advantage of the presented technique is the ability to visually display and design the model of the biometric system, the rapid creation of a working prototype and the reuse of the previously developed functional blocks.

Iris Location Algorithm Based on the CANNY Operator and Gradient Hough Transform

In the iris recognition system, the accuracy of the localization of the inner and outer edges of the iris directly affects the performance of the recognition system, so iris localization has important research meaning. Our iris data contain eyelid, eyelashes, light spot and other noise, even the gray transformation of the images is not obvious, so the general methods of iris location are unable to realize the iris location. The method of the iris location based on Canny operator and gradient Hough transform is proposed. Firstly, the images are pre-processed; then, calculating the gradient information of images, the inner and outer edges of iris are coarse positioned using Canny operator; finally, according to the gradient Hough transform to realize precise localization of the inner and outer edge of iris. The experimental results show that our algorithm can achieve the localization of the inner and outer edges of the iris well, and the algorithm has strong anti-interference ability, can greatly reduce the location time and has higher accuracy and stability.

Features Subset Selection using Improved Teaching Learning based Optimisation (ITLBO) Algorithms for Iris Recognition

Objective: Iris recognition is one of the emerging areas as the demand for security in social and personal areas is increasing day by day. The most challenging step in the process of iris recognition is accurate iris localization. Methods/Statistical Analysis: In this paper, we propose an iris recognition method in light of Teaching Learning Based Optimisation (ITLBO) to choose the ideal components subset. The iris information, for the most part, contains a tremendous number of textural elements and a similarly modest number of tests per subject, which make the accurate iris pattern classification challenging. Findings: Feature selection scheme is used to identify the most important and irrelevant features from extracted features set of a relatively high dimension based on some selection criterions. It is not generally handy to gather an extensive number of tests because of some security issues. In this paper, we propose ITLBO to enhance the feature subset determination by consolidating important results from different component choice strategies. Application/Improvements: The principle target of ITLBO is to accomplish an adjust the Recognition Rate (RR), the False Acceptance Rate (FAR), the False Reject Rate (FRR) and they chose feature subset measure. The proposed method is computationally successful with the RR of 97.97 % on the CASIA iris datasets.

Hardware Accelerators for Iris Localization

This paper presents field programmable logic array (FPGA) based hardware accelerators for iris localization, which can be used to accelerate the iris localization task in reliable and affordable embedded iris recognition systems. This work uses edge-map generation and circular Hough transform (CHT) based algorithm to localize irises in the images captured under near infrared (NIR) illumination. The proposed hardware accelerators for iris localization are: 1) Edge-map generation hardware for pupillary boundary detection; 2) Edge-map generation hardware for limbic boundary detection; and 3) CHT hardware for pupillary and limbic boundary detection. These hardware accelerators have processing time of 390.46 μsec, 393.67 μsec and 3.46 msec (average) respectively for an image of 320 × 240 pixels and achieve the iris localization accuracy of 96.52%. The proposed CHT and median filter hardware implementations show better results than the previous work.

Mini-review: Far peripheral vision

The region of far peripheral vision, beyond 60 degrees of visual angle, is important to the evaluation of peripheral dark shadows (negative dysphotopsia) seen by some intraocular lens (IOL) patients. Theoretical calculations show that the limited diameter of an IOL affects ray paths at large angles, leading to a dimming of the main image for small pupils, and to peripheral illumination by light bypassing the IOL for larger pupils. These effects are rarely bothersome, and cataract surgery is highly successful, but there is a need to improve the characterization of far peripheral vision, for both pseudophakic and phakic eyes. Perimetry is the main quantitative test, but the purpose is to evaluate pathologies rather than characterize vision (and object and image regions are no longer uniquely related in the pseudophakic eye). The maximum visual angle is approximately 1050, but there is limited information about variations with age, race, or refractive error (in case there is an unexpected link with the development of myopia), or about how clear cornea, iris location, and the limiting retina are related. Also, the detection of peripheral motion is widely recognized to be important, yet rarely evaluated. Overall, people rarely complain specifically about this visual region, but with “normal” vision including an IOL for >5% of people, and increasing interest in virtual reality and augmented reality, there are new reasons to characterize peripheral vision more completely.

Circumscribed irido‐ciliary melanoma: surgery versus irradiation

SummaryIris tumors include congenital benign cyst, naevi, melanocytomas malignant melanomas, benign adenoma,leiomyomas, metastatic tumors, iris location of leukemia or lymphomas and histiocytic proliferations like iris xanthogranuloma.Patients with metastatic disease rarely develop a bilateral diffuse melanocytic uveal proliferation (BDUMP) which can cause retinal detachment and blindness.The diagnosis of an iris tumor is made by careful medical history, slit lamp examination, UBM ultrasonography and OCT of the iris.In rare instances fine needle aspiration biopsy can be useful but follow up is often the more important test to differentiate stable naevus versus malignant melanoma of the iris.Treatment can be a simple observation for naevi or melanocytoma..Surgery is still the best option for adenomas and leiomyomas. Metastatic disease can be treated by chemotherapy and external beam radiation. Xanthogranulomas of the iris usually respond well to steroids. For melanoma of the iris a lot of centers do not use surgery and prefer radiotherapy most often proton beam radiotherapy. Results of proton beam therapy of iris melanoma will be presented.

Developing Iris Recognition System for Smartphone Security

Smartphones have become an important way to store sensitive information; therefore, users’ privacy needs to be highly protected. This can be done by using the most reliable and accurate biometric identification system available today: iris recognition. This paper develops and tests an iris recognition system for smartphones. The system uses eye images that rely on visible wavelength; these images are acquired by the smartphone built-in camera. The development of the system passes through four main phases: the first phase is the iris segmentation phase, which is done in three steps to detect the iris region from the captured image, which contains the eye and part of the face using Haar Cascade Classifier training, pupil localization, and iris localization using a Circular Hough Transform. In the second phase, the system applies normalization using a Rubber Sheet model, which converts the iris image to a fixed size pattern. In the third phase, unique features are extracted from that pattern using a Deep Sparse Filtering algorithm. Finally, in the matching phase, seven different matching techniques are investigated to decide the most appropriate one the system will use to verify the user. Two types of testing are conducted: Offline and Online tests. The BIPLab database and a collected dataset are used to measure the accuracy of the system phases and to calculate the Equal Error Rate (EER) for the whole system. The average EER is 0.18 for the BIPLab database and 0.26 for the collected dataset.

Efficient and Fast Iris Localization Using Binary Radial Gradient Features for Human–Computer Interaction

This paper proposes an efficient and fast iris localization method. It uses support vector machine learning of iris features that represent closed outer and inner iris boundaries encompassing a low-intensity region. In addition, depending on the location of the iris in an eye image, an iris detection method is proposed based on three sub-datasets of eye images (middle, right, and left sub-datasets) with different iris features. The proposed method is implemented using fast sliding window and fast computation of the iris detection score with binary features. Compared with state-of-the-art methods, experimental results show that the proposed method is twice as fast and has comparable accuracy, even when factoring in head rotation, glasses, and highlights.

Iris localization by means of adaptive thresholding and Circular Hough Transform

In this paper, a new iris localization method for mobile devices is presented. Our system uses both intensity and saturation threshold on the captured eye images to determine iris boundary and sclera area, respectively. Estimated iris boundary pixels which have been placed outside the sclera will be removed. The remaining pixels are mainly the boundary of iris inside the sclera. Then, circular Hough transform is applied to such iris boundary pixels in order to localize the iris. Experiments were done on 60 iris images taken by a HTC mobile device from 10 different persons with both left and right eyes images available per person. Also, we evaluate the proposed algorithm on MICHE datasets include iphone5, Samsung Galaxy S4 and Samsung Galaxy Tab2. Experimental evaluation shows that the proposed system can successfully localize iris on tested images.

눈 깜박임 화소 값 기반의 안면과 홍채영역 영상인식용 모듈설계

본 논문에서는 홍채정보로 개인인증을 위한 USB-OTG(Uiversal Serial Bus On-the-go) 영상인식 모듈을 설계한다. 개인인증을 위해 사용자가 스마트 폰 버튼을 누를 필요가 없도록 스마트 기기를 안면주위의 여러 장의 안면영상을 획득 후, 눈 깜박임에 의한 화소 값 차로 안면과 홍채영역을 검색하는 영상인식 알고리듬을 제안한다. 본 연구에서는 인접한 눈을 뜬 영상과 눈을 감은 영상을 감지한 안면과 홍채 영상의 프레임 화소 값의 차이를 사용한다. 또한, 홍채 영역분할에 의한 동공과 홍채영역 위치를 빠르게 찾을 수 방법을 활용한다. 제안한 빠른 홍채영역의 위치탐색은 눈 영역의 적정한 그리드 크기에 의해 결정할 수 있다. 안면과 홍채영역의 제한된 영역을 탐색하는 홍채인식 카메라 모듈의 USB-OTG 인터페이스 통한 인접영상의 프레임 차이에 의해 검출할 수 있도록 설계하였다. 이로서 스마트 디바이스 사용자가 홍채 인식을 위해 눈을 깜빡이지 않고 대기해야 하는 불편함을 제거함으로써 사용자 편의성을 증대시킬 것으로 기대한다. In this paper, an USB-OTG (Uiversal Serial Bus On-the-go) interface module was designed with the iris information for personal identification. The image recognition algorithm which was searching face and iris areas, was proposed with pixel differences from eye blinking after several facial images were captured and then detected without any activities like as pressing the button of smart phone. The region of pupil and iris could be fast involved with the proper iris area segmentation from the pixel value calculation of frame difference among the images which were detected with two adjacent open-eye and close-eye pictures. This proposed iris recognition could be fast processed with the proper grid size of the eye region, and designed with the frame difference between the adjacent images from the USB-OTG interface with this camera module with the restrict of searching area in face and iris location. As a result, the detection time of iris location can be reduced, and this module can be expected with eliminating the standby time of eye-open.

Iris Localization Method Based on SIFT and SDM

Iris Localization Method Based on SIFT and SDM

Efficient iris localization and recognition

Iris localization is an important step in iris recognition systems; all the subsequent steps, iris normalization, feature extraction and matching, depend on its accuracy. Traditional iris localization methods often involve an exhaustive search of a three-dimensional parameter space, which is a time consuming process. This paper presents a coarse-to-fine algorithm to address the computational cost problem, while achieving an acceptable accuracy. The iris gray image is transformed to a binary image using an adaptive threshold obtained from analyzing the image intensity histogram. Morphological processing is then used to extract an initial center point, which is considered as the initial center for both pupil and iris boundaries. Finally, a refinement step is made using an integro-differential operator to get the final iris and pupil centers and radii. This system proves to be robust against occlusions and intensity variations.

Efficient Iris Localization via Optimization Model

Iris localization is one of the most important processes in iris recognition. Because of different kinds of noises in iris image, the localization result may be wrong. Besides this, localization process is time-consuming. To solve these problems, this paper develops an efficient iris localization algorithm via optimization model. Firstly, the localization problem is modeled by an optimization model. Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization. And SDM (Supervised Descent Method) algorithm is employed to solve the final points of outer boundary and eyelids. Finally, IRLS (Iterative Reweighted Least-Square) is used to obtain the parameters of outer boundary and upper and lower eyelids. Experimental result indicates that the proposed algorithm is efficient and effective.

Non-circular iris contours localization in the visible wavelength eye images

Contemporary iris localization schemes perform well for ideal eye images generally acquired using near infrared (NIR) illumination, but perform poorly for non-ideal eye images acquired using Visible Wavelength (VW) illumination. Non-ideal eye images contain noise such as non-uniform illumination, defocus, etc. This study proposes a reliable iris localization algorithm, in particular for VW eye images. First, it suppresses light reflections and sharp intensity variations in the Red-component of input color eye image. Next, it marks a set of potential pixels (ROIpixels) suitable as the iris center's candidates. Using ROIpixels, it localizes potential iris location using a hybrid of Integro-differential operator (IDO) and image intensity. Then, it reuses IDO and iris/pupil's geometry to localize pupil within iris. Finally, it uses the Fourier series based scheme to regularize iris contours. Experimental results obtained on UBIRIS V1.0, MMU V1.0, and IITD V1.0 iris databases demonstrate its superior performance compared with contemporary schemes.

High Biometric Recognition Based on Histogram and Semi-discrete Matrix Decomposition via Neural Network

Iris recognition is regarded as the most reliable and accurate biometric identification system available highly protected and stable.Iris situating is the main focus in the procedure of iris recognition and verifies the precision of identification. In this work, a new algorithmfor iris localization is suggested based on the median filter and the histogram to determine an automated global threshold and the pupil centre. An algebraic based on semi-discrete matrix decomposition SDD is used to extract iris feature from iris image that decrease the difficultyininput layered neural network,thesizes of input patterns are enhanced. The iris recognition is developed by a neural network with differential adaptive learning rate to identify the iris features. This method is simple,effective and high speed recognition. The system is implemented by using Matlab. Experimental outcomes indicate that the suggested algorithm gives the accuracy of 100 % with time equal 1.4 sec is best than other methods for Daugman and Wildes.