Iris Localization Algorithm Research Articles

A non-circular iris localization algorithm using image projection function and gray level statistics

Iris recognition technology identifies an individual from its iris texture with great precision. A typical iris recognition system comprises eye image acquisition, iris segmentation, feature extraction, and matching. However, the system precision greatly depends on accurate iris localization in the segmentation module. In this paper, we propose a reliable iris localization algorithm. First, we locate a coarse eye location in an eye image using integral projection function (IPF). Next, we localize the pupillary boundary in a sub image using a reliable technique based on the histogram-bisection, image statistics, eccentricity, and object geometry. After that, we localize the limbic boundary using a robust scheme based on the radial gradients and an error distance transform. Finally, we regularize the actual iris boundaries using active contours. The proposed algorithm is tested on public iris databases: MMU V1.0, CASIA-IrisV1, and the CASIA-IrisV3-Lamp. Experimental results demonstrate superiority of the proposed algorithm over some of the contemporary techniques.

Reliable iris localization using Hough transform, histogram-bisection, and eccentricity

The iris technology recognizes individuals from their iris texture with great precision. However, it does not perform well for the non-ideal data, where the eye image may contain non-ideal issues such as the off-axis eye image, blurring, non-uniform illumination, hair, glasses, etc. It is because of their iris localization algorithms, which are developed for the ideal data. In this paper, we propose a reliable iris localization algorithm. It includes localizing a coarse iris location in the eye image using the Hough transform and image statistics; localizing the pupillary boundary using a bi-valued adaptive threshold and the two-dimensional (2D) shape properties; localizing the limbic boundary by reusing the Hough accumulator and image statistics; and finally, regularizing these boundaries using a technique based on the Fourier series and radial gradients. The proposed technique is tested on the public iris databases: CASIA V1, CASIA-IrisV3-Lamp, CASIA-IrisV4-Thousand, IITD V1.0, MMU V1.0, and MMU (new) V2.0. Experimental results obtained on these databases show superiority of the proposed technique over some state of the art iris localization techniques.

Iris localization in frontal eye images for less constrained iris recognition systems

Commercial iris recognition systems do not perform well for non-ideal data, because their iris localization algorithms are specifically developed for controlled data. This paper presents a robust iris localization algorithm for less constrained data. It includes: (i) suppressing specular reflections; (ii) localizing the iris inner (pupil circle) and outer (iris circle) boundaries in a two-phase strategy. In the first phase, we use Hough transform, gray level statistics, adaptive thresholding, and a geometrical transform to extract the pupil circle in a sub-image containing a coarse pupil region. After that, we localize iris circle in a sub-image centered at the pupil circle. However, if the first phase fails, the second phase starts, where first we localize a coarse iris region in the eye image. Next, we extract pupil circle within the coarse iris region by reusing procedure of first phase. Following that, we localize iris circle. In either of the two phases, we validate the pupil location by using an effective occlusion transform; and (iii) regularizing the iris circular boundaries by using radial gradients and the active contours. Experimental results show that the proposed technique is tolerant to off-axis eye images, specular reflections, non-uniform illumination; glasses, contact lens, hair, eyelashes, and eyelids occlusions.

应用统计距离实现虹膜定位

A Method of Counting Distances(MCD) was proposed to improve the efficiency of iris recognition in this paper.The MCD was used in a denoising for iris edge image,and efficient location methods for both inner and outer circles of the iris were established.First,a method of grey level summation was utilized to find a pixel in the pupil,meanwhile,the iris edge image could be obtained by using the Canny operator,and three edge points of iris inner edge could be found according to the pixel in the pupil.Then,three edge points were derived to the equation of a circle to find the iris inner edge by substituting.Furthermore,the noises in iris outer edge image were eliminated in terms of the inner location data,and 20 percent of the edge points far from the center of inner circle of the iris were excluded from the denoised edge image.Finally,an improved Hough transformation was used to obtain the data of iris outer edge.Experimental results show that both inner and outer location time obtained using MCD is lower than those obtained by the Fast and Accurate Iris Location Algorithm(FAILA),and the success rate of MCD is 96.67%,which is higher than that of FAILA.Thus,the MCD has high precision and fast speed,and can lay the foundation for improving the efficiency of iris recognition.

The Research of the Improvement of Iris Location of the Sobel Algorithm

An improved sobel algorithm is proposed for the iris location algorithm,solving the problem of large amount of computation in standard iris location algorithm as well as location impreciseness.According to the geometric character of human eyes for iris location,select purposely the special region of sub-image as the target region, then apply the edge detection,projection for rough location,then use the improved sobel algorithm to fine position.This improved sobel algorithm not only use the advantages of the original algorithm,but also make sobel algorithms only to detect circle.Improve the accuracy of iris location and make more accurate positioning.Experimental results showed that the algorithm with less computational can be accurately positioned inside and outside the boundary of the iris.

New algorithm for iris recognition based on video sequences

Among existing biometrics, iris recognition systems are among the most accurate personal biometric identification systems. However, the acquisition of a workable iris image requires strict cooperation of the user; otherwise, the image will be rejected by a verification module because of its poor quality, inducing a high false reject rate (FRR). The FRR may also increase when iris localization fails or when the pupil is too dilated. To improve the existing methods, we propose to use video sequences acquired in real time by a camera. In order to keep the same computational load to identify the iris, we propose a new method to estimate the iris characteristics. First, we propose a new iris texture characterization based on Fourier-Mellin transform, which is less sensitive to pupil dilatations than previous methods. Then, we develop a new iris localization algorithm that is robust to variations of quality (partial occlusions due to eyelids and eyelashes, light reflects, etc.), and finally, we introduce a fast and new criterion of suitable image selection from an iris video sequence for an accurate recognition. The accuracy of each step of the algorithm in the whole proposed recognition process is tested and evaluated using our own iris video database and several public image databases, such as CASIA, UBIRIS, and BATH.

A NOVEL IRIS LOCATION ALGORITHM

Biometric technologies are becoming much more important in various applications. Among them, iris recognition is considered as one of the most reliable and accurate technologies. In the preparation of iris recognition, the iris location will influence the performance of the entire system. This paper proposes a novel algorithm to locate iris and eyelids. Morphological operation is applied to remove eyelashes during iris boundary location. An optimal step length is calculated to reduce the searching time. Experimental results demonstrate that the proposed iris location algorithm is able to achieve a good performance with accuracy higher than 97.6%.

Improved fast iris localization algorithm

For the purpose of detecting iris precisely and quickly,an ellipse pupil based detection algorithm was proposed based on some prevailing localization algorithms.After determining the threshold by OTSU algorithm,the longest-chord based method detected the boundary of pupil more effectively than the prevailing algorithms based on circular template.The outer boundary of iris based on circular template was searched in less range as the pupil and iris were approximately concentric.Compared with the usual algorithms based on circular template,this algorithm detects pupil as ellipse,reduces the blindness of the search process,and improves the speed and accuracy.

Robust tracking and remapping of eye appearance with passive computer vision

A single-camera iris-tracking and remapping approach based on passive computer vision is presented. Tracking is aimed at obtaining accurate and robust measurements of the iris/pupil position. To this purpose, a robust method for ellipse fitting is used, employing search constraints so as to achieve better performance with respect to the standard RANSAC algorithm. Tracking also embeds an iris localization algorithm (working as a bootstrap multiple-hypotheses generation step), and a blink detector that can detect voluntary eye blinks in human-computer interaction applications. On-screen remapping incorporates a head-tracking method capable of compensating for small user-head movements. The approach operates in real time under different light conditions and in the presence of distractors. An extensive set of experiments is presented and discussed. In particular, an evaluation method for the choice of layout of both hardware components and calibration points is described. Experiments also investigate the importance of providing a visual feedback to the user, and the benefits gained from performing head compensation, especially during image-to-screen map calibration.

An improved method for Daugman's iris localization algorithm

Computer-based automatic recognition of persons for security reasons is highly desirable. Iris patterns provide an opportunity for separation of individuals to an extent that would avoid false positives and negatives. The current standard for this science is Daugman's iris localization algorithm. Part of the time required for analysis and comparison with other images relates to eyelid and eyelash positioning and length. We sought to remove the upper and lower eyelids and eyelashes to determine if separation of individuals could still be attained. Our experiments suggest separation can be achieved as effectively and more quickly by removing distracting and variable features while retaining enough stable factors in the iris to enable accurate identification.

A Novel and Efficient Method for Iris Automatic Location

Abstract An efficient and robust iris location algorithm plays a very important role in a real iris recognition system. A novel and efficient iris automatic location method is presented in this study. It includes following two steps mainly: pupil location and iris outer boundary location. A digital eye image was divided into many small rectangular blocks with fixed size in the pupil location, and the block with the smallest average intensity was selected as a reference area. Then image binarization was implemented taking the average intensity of the reference area as a threshold. At last the center coordinates and radius of pupil were estimated by extending the reference area to the pupil's boundaries in the binary iris image. In the iris outer location, two local parts of the eye image were selected and transformed into polar coordinates from Cartesian reference. In order to detect the fainter outer boundary of the iris quickly, a novel edge detector was used to locate boundaries of the two parts. The center coordinates and radius of the iris outer boundary can be estimated using the fusion of the locating results of the two local parts and the location information of the pupil. The algorithm was tested on CASIA v1.0 and MMU v1.0 digital eye image databases and experimental results show that the proposed method has satisfying performance and good robustness.