Iris localization based on the Hough transform,a radial-gradient operator,and the gray-level intensity

The iris biometric recognizes a human based on his/her iris texture, which is a stable and unique feature for every individual. A typical iris biometric system performs better for the ideal data, which is acquired under controlled conditions. However, its performance degrades when localizing iris in non-ideal data containing the noisy issues, e.g., the non-uniform illumination, defocus, and non-circular iris boundaries. This study proposes a reliable algorithm to localize iris in such images robustly. First, a small region containing the coarse location of iris is localized. Next, the pupillary boundary is extracted within this small region using an iterative-scheme comprising an adaptive binarization and a pupil location verification test. Following that, the limbic boundary is localized by reusing the Hough accumulator. The iris location is also verified through a gray-level test. After that, the pupillary and limbic boundaries are regularized by applying an enhanced method comprising a Radial-gradient operator (RGO), an error-transform (ET), and the Fourier series. Experimental results, obtained on the CASIA-IrisV3, CASIA-IrisV4, MMU V1.0, and MMU(new) V2.0 iris databases, show superiority of the proposed technique over some of the contemporary techniques.     

Localization of iris in gray scale images using intensity gradient

A new method of iris localization based on intensity value analysis is proposed in this paper. Iris recognition systems are mainly dependent on the performance of iris localization processing. Steps after localization involve normalization, feature extraction and matching. These steps are based on the accuracy and efficiency of localization of iris in human eye images. In the proposed scheme, the inner boundary of iris is calculated by finding the pupil center and radius using two methods. In the first method, selected region is adaptively binarized and centroid of the region utilized for obtaining pupil parameters. Edges are processed to detect radius and center of pupil during the second method. For outer iris boundary, a band is calculated within which iris outer boundary lies. Signals in one dimension are picked up along radial direction within determined band at different angles. Three points with maximum gradient are selected from each signal. Redundant points are deleted using Mahalanobis distance and remaining points are used to obtain the outer circle of the iris. Points for upper and lower eyelids are found in the same way as the iris outer boundary. Selected points are then statistically fitted to make parabolas and lastly eyelashes are removed from the image to completely localize the iris. Experimental results show that proposed method is very efficient and accurate.     

Iris segmentation for visible wavelength and near infrared eye images

State-of-the-art iris segmentation algorithms exhibit poor performance for non-ideal data, which is mainly because of the noise such as low contrast, non-uniform illumination, reflections, and among others. To address this issue, a robust iris segmentation scheme is proposed that includes the following: First, a set of the Seed-pixels in a preprocessed eye image is marked adaptively. Next, a two-fold scheme based on a Circu-differential accumulator (CDA) and gray statistics is adopted to localize coarse iris region robustly. Notably, the proposed CDA has close resemblance with the Hough transform; however, it consumes relatively less memory and is free from thresholding as well. Similarly, pupillary boundary is localized, which is verified through an intensity test as well. Next, a refine estimate for the limbic boundary is extracted. After that, iris boundaries are regularized using the Fourier series. Finally, the eyelids are localized using a Para-differential accumulator (PDA), and eyelashes and reflections are also localized adaptively in the polar form of iris. Experimental results on the near infrared (NIR) and visible wavelength (VW) iris databases show that the proposed technique outperforms contemporary approaches.     

Automatic localization of pupil using eccentricity and iris using gradient based method

This paper presents a novel approach for the automatic localization of pupil and iris. Pupil and iris are nearly circular regions, which are surrounded by sclera, eyelids and eyelashes. The localization of both pupil and iris is extremely important in any iris recognition system. In the proposed algorithm pupil is localized using Eccentricity based Bisection method which looks for the region that has the highest probability of having pupil. While iris localization is carried out in two steps. In the first step, iris image is directionally segmented and a noise free region (region of interest) is extracted. In the second step, angular lines in the region of interest are extracted and the edge points of iris outer boundary are found through the gradient of these lines. The proposed method is tested on CASIA ver 1.0 and MMU Iris databases. Experimental results show that this method is comparatively accurate.     

Iris segmentation using variational level set method

Continuous efforts have been made to process degraded iris images for enhancement of the iris recognition performance in unconstrained situations. Recently, many researchers have focused on developing the iris segmentation techniques, which can deal with iris images in a non-cooperative environment where the probability of acquiring unideal iris images is very high due to gaze deviation, noise, blurring, and occlusion by eyelashes, eyelids, glasses, and hair. Although there have been many iris segmentation methods, most focus primarily on the accurate detection of iris images captured in a closely controlled environment. The novelty of this research effort is that we propose to apply a variational level set-based curve evolution scheme that uses a significantly larger time step to numerically solve the evolution partial differential equation (PDE) for segmentation of an unideal iris image accurately, and thereby, speeding up the curve evolution process drastically. The iris boundary represented by the variational level set may break and merge naturally during evolution, and thus, the topological changes are handled automatically. The proposed variational model is also robust against poor localization and weak iris/sclera boundaries. In order to solve the size irregularities occurring due to arbitrary shapes of the extracted iris/pupil regions, a simple method is applied based on connection of adjacent contour points. Furthermore, to reduce the noise effect, we apply a pixel-wise adaptive 2D Wiener filter. The verification and identification performance of the proposed scheme is validated on three challenging iris image datasets, namely, the ICE 2005, the WVU Unideal, and the UBIRIS Version 1.     

The recognition and localization of insulators adopting SURF and IFS based on correlation coefficient

The recognition and localization of insulators is a crucial process of insulators’ failure detection. Aiming to identify insulators in test aerial image under the complicated background with no template and segmentation, a novel method of the recognition and localization of insulators based on SURF (Speeded Up Robust Features) and IFS (Intuitionistic Fuzzy Set) based on correlation coefficient is proposed as following: SURF is used to extract a set of feature points of the test aerial image. Then use IFS based on correlation coefficient to partition points into k classes. Find all the connected regions for each class, and calculate minimum circumscribed rectangle and shape characteristic value for each region to identify insulators, and localize them by rectangular boxes. Thus we can detect all the insulators from test aerial image correctly. Experimental results show that the proposed approach can obtain high precision in the recognition and localization of insulators.     

基于特定感兴趣区采样的虹膜定位改进算法

从虹膜图像特点出发,以瞳孔形心为辅助点实现对虹膜图像特定感兴趣区采样.并且根据虹膜内外圆半径的生理特点设定Hough变换半径参量,依据虹膜内外圆近似同心圆来筛选与虹膜外圆最匹配的Hough变换结果.该算法在主动避开眼皮,睫毛干扰的同时又显著降低了Hough变换的计算量.通过对中科院自动化所CASIA虹膜数据库108组图像的虹膜定位测试结果表明,该方法平均定位时间0.83 s,定位准确率98.9%.     

一种基于局部信息统计的虹膜分块编码方法

由于虹膜自身的稳定性、非侵犯性、不可更改性等优点,虹膜识别已经成为生物特征身份鉴别领域中的研究热点。但虹膜丰富的纹理和复杂的结构给特征提取和编码带来了很大困难。为尽可能地简化特征提取和编码方法,提高虹膜识别效率,提出了一种基于局部信息统计的虹膜分块编码方法。对原始人眼图像进行虹膜定位等预处理操作,得到归一化的虹膜纹理图像;分别根据虹膜局部信息与全局信息、局部信息与局部信息之间的比较关系进行分块编码;计算了不同虹膜代码之间的汉明(Hamming)距离。根据汉明距离给出识别结果。实验证明该方法有效、可行,具有较高的识别率和识别速度。     

A double random phase encoding approach for cancelable iris recognition

In a cancelable iris recognition technique, all enrollment patterns are masked using a transformation function, and the invertibility process for obtaining the original data should not be possible. A novel cancelable iris authentication approach in the encrypted domain is presented in this paper. The double random phase encoding (DRPE) algorithm in the Fractional Fourier Transform (FrFT) Domain is utilized to generate the optical masked IrisCodes. For the transmitter side, two encryption keys (RPM1 and RPM2) are utilized, while the second phase mask is proposed to be the right iris feature vector of the same user. As a result, mixing the feature vectors of the left and right iris patterns of the same subject to an encrypted IrisCode results in enhancing the privacy and preserving the system performance. This proposed system success is attributed to the fact that the iris authentication issue is transformed to a key authentication process. Experimental results conducted on CASIA-IrisV3-Interval dataset achieve a significant gain for both privacy and performance proving the superiority of the proposed approach.     

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.     

用多消失矩最优小波包基改进虹膜识别

采用层叠算法求出尺度函数和小波函数的离散采样序列的逼进，将不具有基函数解析表达式的母小波用于虹膜特征提取，并利于实现连续小波包变换.针对虹膜光学识别应用背景，提出采用基于连续小波包变换的多消失矩联合最优小波包基来改进特征图像相关识别的最优基，并用统计识别方法进行后处理以增强算法适应性，同时提出通过体全息相关系统来实现，以发挥光学高并行性的优势，模拟结果表明可获得比已有方法更高的识别率.     

高精度自动贴片机视觉系统定位算法研究

针对高精度自动贴片机的关键技术视觉识别定位算法进行了研究,针对贴片机PCB板标记点及元件图像的特点,提出了结合图像分割、几何特征识别与模式匹配的图像识别及定位方法。根据以上算法开发了针对视觉定位系统的图像识别软件,并用PCB板图像及元件图像进行识别定位验证。实验结果表明,该算法能够有效地完成贴片机视觉系统的识别定位任务。     

光学小波包变换及其滤波器的研究

基于对光学小波变换必要条件的分析,提出光学小波包变换的概念.选出虹膜图库的联合最优小波包基,利用最优基的线性组合生成相应的复合光学小波包滤波器.将滤波器用于光电混合虹膜识别系统中对待识别输入进行小波包特征提取预处理,模拟结果不仅证明引入该滤波器可明显提升系统的识别效果,也证明了光学小波包变换提出的意义.     

基于人眼灰度分布特征的虹膜定位算法

虹膜定位是虹膜识别过程中的重要环节,定位速度和精度决定了整个虹膜识别系统的性能。提出了一种基于人眼灰度分布特征的虹膜定位算法,该算法利用形态学运算实现瞳孔圆心粗定位,采用划分区域求灰度均值隔项差值最大值的方法实现外圆半径的粗定位,并通过分层聚类的方法实现内外边界的精确定位。实验结果表明．与经典的虹膜定位算法如Wildes算法、Daugman算法相比,该算法定位结果更加准确,定位速度大幅度提高。     

Human eye iris recognition using the mutual information

This article presents the new biometric electro-optical measuring method supported by PC for identification of a person by its eye iris image recognition. The aim of this approach is to show the ability of mutual information to such recognition. Couples of the comparative human iris images were geometrically aligned by maximization of their mutual information and subsequently recognized. The mutual information was estimated using the efficient algorithm proposed by G. A. Darbellay and I. Vajda. The decision whether two compared iris images belong to the same eye depends on the chosen threshold of the mutual information. Only a simple preprocessing of the iris images is needed to acquire their characteristic geometric parameters before the recognition is performed. The investigations carried out showed the correct (successful) personal identification better than 99%.     

A novel algorithm for automatic localization of human eyes

Based on geometrical facial features and image segmentation, we present a novel algorithm for automatic localization of human eyes in grayscale or color still images with complex background. Firstly, a determination criterion of eye location is established by the prior knowledge of geometrical facial features. Secondly, a range of threshold values that would separate eye blocks from others in a segmented face image (i.e., a binary image) are estimated. Thirdly, with the progressive increase of the threshold by an appropriate step in that range, once two eye blocks appear from the segmented image, they will be detected by the determination criterion of eye location. Finally, the 2D correlation coefficient is used as a symmetry similarity measure to check the factuality of the two detected eyes. To avoid the background interference, skin color segmentation can be applied in order to enhance the accuracy of eye detection. The experimental results demonstrate the high efficiency of the algorithm and correct     

基于暗瞳图像的人眼视线估计

虹膜外边缘受眼睑遮挡较为严重时, 会给虹膜中心的准确提取造成很大的困难. 为此, 提出利用放置在相机轴外的红外光源产生的暗瞳图像估计瞳孔中心, 该方法避免了提取虹膜外边缘遇到的遮挡问题. 首先利用角膜反射光斑在相机像面中的位置估计角膜所在球体中心的三维空间坐标, 作为眼球的平动信息; 然后考察瞳孔中心与角膜球体中心在相机成像面投影位置的相对偏移, 作为眼球的转动信息; 最后利用人工神经网络完成视线特征向量与注视点坐标间的映射. 在人眼区域定位的问题上, 利用两部大视场相机, 采用自适应增强算法和主动表观模型算法实现眼部区域的准确定位, 该步骤可以将提取反射光斑和瞳孔中心需要考虑的图像区域限定在较小的范围内. 实验结果表明, 本文视线估计方法在水平方向上的平均误差为0.62°, 在竖直方向上的平均误差为1.05°, 是解决视线点估计的有效方法. 关键词： 暗瞳 人工神经网络 自适应增强 主动表观模型     

Cellular neural networks for welding arc thermograms segmentation

Machine vision systems are used in many areas for monitoring of technological processes. Among this processes welding takes important place, where often infrared cameras are used. Besides reliable hardware, successful application of vision systems requires suitable software based on proper algorithms. One of most important group of image processing algorithms is connected to image segmentation. Obtainment of exact boundary of an object that changes shape in time, such as the welding arc, represented on a thermogram is not a trivial task. In the paper a segmentation method using supervised approach based on a cellular neural networks is presented. Simulated annealing and genetic algorithm were used for training of the network (template optimization). Comparison of proposed method to a well elaborated segmentation method based on region growing approach was made. Obtained results prove that the cellular neural network can be a valuable tool for infrared welding pool images segmentation.     

混沌加密的虹膜识别系统的安全性

提出了针对特征码的单向耦合映像格混沌加密系统,以及Arnold变换与单向耦合映像格混沌系统相结合对虹膜信息进行加密的复合混沌加密系统.利用Arnold变换置乱并混淆虹膜图像,通过镜像解决零点不变性问题,最后通过单向耦合映像格混沌系统实现加密.实验表明本算法密钥空间大,统计特性好,密钥敏感性强,加密速度快,平均加密时间70 ms.     

基于K-L变换的虹膜识别方法

提出了一种基于K-L变换和最近邻分类器的虹膜识别方法。该方法采用K-L变换得到一组虹膜图像基,并利用这组基图像构造一子空间,将待识别图像在这个空间上的投影系数作为待识别图像的特征向量。采用最近邻分类器进行了分类。基于CASIA虹膜数据库的试验表明,将K-L变换应用在虹膜特征提取上可以取得较高的识别率。