结合形态学的Hough变换虹膜定位改进算法

虹膜定位是在虹膜图像中确定虹膜的内外边界,是虹膜识别过程的首要环节。Hough变换是虹膜定位的经典算法,但对原始图像质量要求高,算法运算时间长。依据人眼图像的灰度特性,结合形态学处理提出一种改进的Hough变换定位新算法。对图像进行灰度二值化运算后进行形态学处理分离出瞳孔,结合Sobel算子边缘检测出瞳孔边界点,通过最小二乘法拟合定位出虹膜内边界;在先验知识和形态学处理的基础上对图像进行Hough变换,定位出虹膜的外边界。实验表明所提出的算法性能比传统Hough变换有较大提高,可用于实际虹膜识别的预处理过程中。     

Circular shortest path as a method of detection and refinement of iris borders in eye image

Circular shortest path detection method is tried for locating pupil and iris boundaries in eye image. Two types of its application are tested: detecting pupil and iris boundaries using given approximate eye center point and refining pupil boundaries using given approximate pupil circle. Brightness gradient direction was used to choose image pixels, which may belong to pupil or iris boundary. The method seems to have worse performance in the detection task compared to other known approaches doing the same, but appears to be useful in the refinement task. The method was tested with public domain iris databases, totally with more than 80000 images for the first type of application and with more than 16000 images for the second type.     

基于微积分法的虹膜边缘检测技术的研究

当前社会,人们对于身份安全越来越重视,尤其是在一些高度保密或者涉及个人隐私方面的场合,一对一的身份识别显得尤为重要。而虹膜识别恰好具备高效、不易被仿造等特点,使其作为一项身份识别技术被推向了热潮。图像边缘检测一直是图像处理中的经典研究课题,也是至今仍没有得到圆满解决的一类问题。因此,探讨获取图像的边缘和轮廓的问题,是图像工程师们的重中之重。而虹膜识别技术中的边缘检测也如上所说是重要的一项技术,虹膜的内外边界可以近似地用圆来拟合。内圆表示虹膜与瞳孔的边界,外圆表示虹膜与巩膜的边界,但是这两个圆并不是同心圆。而如何更好、更准确地且不受外界干扰以及图像模糊情况下仍能较为有效地进行内外圆的边缘检测是研究的重点。文中就微积分法用于虹膜边缘检测方面展开了研究。     

A highly accurate and computationally efficient approach for unconstrained iris segmentation

Biometric research has experienced significant advances in recent years given the need for more stringent security requirements. More important is the need to overcome the rigid constraints necessitated by the practical implementation of sensible but effective security methods such as iris recognition. An inventive iris acquisition method with less constrained image taking conditions can impose minimal to no constraints on the iris verification and identification process as well as on the subject. Consequently, to provide acceptable measures of accuracy, it is critical for such an iris recognition system to be complemented by a robust iris segmentation approach to overcome various noise effects introduced through image capture under different recording environments and scenarios. This research introduces a robust and fast segmentation approach towards less constrained iris recognition using noisy images contained in the UBIRIS.v2 database (the second version of the UBIRIS noisy iris database). The proposed algorithm consists of five steps, which include: (1) detecting the approximate localization of the eye area of the noisy image captured at the visible wavelength using the extracted sclera area, (2) defining the outer iris boundary which is the boundary between iris and sclera, (3) detecting the upper and lower eyelids, (4) conducting the verification and correction for outer iris boundary detection and (5) detecting the pupil area and eyelashes and providing means for verification of the reliability of the segmentation results. The results demonstrate that the accuracy is estimated as 98% when using 500 randomly selected images from the UBIRIS.v2 partial database, and estimated at ?97%$97\%$ in a “Noisy Iris Challenge Evaluation (NICE.I)” in an international competition that involved 97 participants worldwide, ranking this research group in sixth position. This accuracy is achieved with a processing speed nearing real time.     

基于分块搜索的虹膜定位算法

针对传统虹膜定位算法识别效果不稳定,鲁棒性低的问题,提出基于分块搜索的虹膜定位算法。首先利用虹膜图像灰度变化差异将虹膜图像转换为二值图像,用基于边缘检测的hough圆检测法粗略定位虹膜内圆,再利用分块搜索二值图像对内圆进行精确定位。之后利用卷积运算粗略定位外圆,再对原图像进行分块搜索,观察截图的灰度直方图中的灰度变化精确定位外圆。将得到的虹膜与传统定位算法得到的虹膜用相同的虹膜识别算法处理,结果表明,该算法定位出的图像识别上效果更明显,并且具有很好的鲁棒性。     

基于彩色分割的虹膜检测

提出了一种基于彩色分割的虹膜检测方法,对于一幅经过准确定位的眼睛窗口图像,它的彩色信息比灰度信息更有助于进行虹膜检测。首先利用图像颜色的饱和度信息将眼睛图像中的眼睛区域与皮肤区域分离,然后利用亮度信息将眼睛区域的眼白和虹膜分离得到虹膜区域,再通过Hough变换进行虹膜检测。结果证明该方法不仅检测率高,而且能适应一定人脸姿势、眼睛视线方向的变化,甚至在眼睛区域较暗的情况下,也能很好地检测到虹膜。     

虹膜内外边缘的快速定位算法

虹膜定位是虹膜识别过程中的重要环节,定位的速度和精度决定了整个虹膜识别系统的性能。论文提出一种基于Hough变换的虹膜定位算法,根据虹膜图像特点,先对其进行预处理,再用灰度投影法粗定瞳孔圆心;通过对虹膜内外边缘的“先采样后变换”,减少Hough变换的运算量;利用虹膜内外边缘之间的耦合关系,由内至外精确地确定出边缘。对300多幅虹膜样本的处理结果表明,此算法快速、精确、鲁棒。     

Eye detection in a facial image under pose variation based on multi-scale iris shape feature

The accurate location of eyes in a facial image is important to many human facial recognition-related applications, and has attracted considerable research interest in computer vision. However, most prevalent methods are based on the frontal pose of the face, where applying them to non-frontal poses can yield erroneous results.In this paper, we propose an eye detection method that can locate the eyes in facial images captured at various head poses. Our proposed method consists of two stages: eye candidate detection and eye candidate verification. In eye candidate detection, eye candidates are obtained by using multi-scale iris shape features and integral image. The size of the iris in face images varies as the head pose changes, and the proposed multi-scale iris shape feature method can detect the eyes in such cases. Since it utilizes the integral image, its computational cost is relatively low. The extracted eye candidates are then verified in the eye candidate verification stage using a support vector machine (SVM) based on the feature-level fusion of a histogram of oriented gradients (HOG) and cell mean intensity features.We tested the performance of the proposed method using the Chinese Academy of Sciences' Pose, Expression, Accessories, and Lighting (CAS-PEAL) database and the Pointing'04 database. The results confirmed the superiority of our method over the conventional Haar-like detector and two hybrid eye detectors under relatively extreme head pose variations.     

一种有效的虹膜定位及睫毛检测方法

提出一种有效的虹膜定位及睫毛检测方法。通过把眼睛图像中分割成小的矩形区域,利用找到的这些矩形区域像素平均最小值把眼睛图像进行二值化,找到虹膜区域的内边界;以瞳孔的质心为参考点,在其左右的扇形区域内分别使用修改后的Daugman的检测算子,找到像素值变换大的位置,进而定位出虹膜的外界;使用Gobor滤波器和窗口移动法对睫毛进行有效的检测。通过对大量虹膜图像的实验表明,该方法取得了非常好的结果。     

A dynamic non-circular iris localization technique for non-ideal data

Iris localization plays a decisive role in the overall iris biometric system’s performance, because it isolates the valid part of iris. This study proposes a reliable iris localization technique. It includes the following. First, it extracts the iris inner contour within a sliding-window in an eye image using a multi-valued adaptive threshold and the two-dimensional (2D) properties of binary objects. Then, it localizes the iris outer contour using an edge-detecting operator in a sub image centered at the pupil center. Finally, it regularizes the iris contours to compensate for their non-circular structure. The proposed technique is tested on the following public iris databases: CASA V1.0, CASIA-Iris-Lamp, IITD V1.0, and the MMU V1.0. The experimental and accuracy results of the proposed scheme compared with other state-of-the-art techniques endorse its satisfactory performance.     

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.     

An eye detection method robust to eyeglasses for mobile iris recognition

Finding the accurate position of an eye is crucial for mobile iris recognition system in order to extract the iris region quickly and correctly. Unfortunately, this is very difficult to accomplish when a person is wearing eyeglasses because of the interference from the eyeglasses. This paper proposes an eye detection method that is robust to eyeglass interference in mobile environment. The proposed method comprises two stages: eye candidate generation and eye validation. In the eye candidate generation stage, multi-scale window masks consisting of 2 × 3 subblocks are used to generate all image blocks possibly containing an eye image. In the ensuing eye validation stage, two methods are employed to determine which blocks actually contain true eye images and locate their precise positions as well: the first method searches for the glint of an NIR illuminator on the pupil region. If this first method fails, the next method computes the intensity difference between the assumed pupil and its surrounding region using multi-scale 3 × 3 window masks. Experimental results show that the proposed method detects the eye position more accurately and quickly than competing methods in the presence of interference from eyeglass frames.     

一种基于圆几何特征的虹膜定位算法

虹膜定位是虹膜识别中基础性环节,其精度和速度决定了虹膜识别系统的性能,为提高虹膜定位的速度,提出一种基于圆几何特征的虹膜内边缘定位算法,利用内外边缘中心的耦合特性缩小微积分方法搜索外边缘的范围。试验结果表明,与经典虹膜定位算法相比,本算法快速、精确、鲁棒。     

三次B样条曲线拟合的虹膜定位

采用圆检测定位虹膜内外边界的方法是当前虹膜定位的主流算法.当虹膜图像分辨率很高时,圆曲线不能准确地拟合虹膜真实边界,特别是受瞳孔收缩影响很大的内边界.而采用三次B样条曲线能够很好地拟合内边界.为了提高定位效率,首先运用质心探测方法分割出瞳孔区域,然后在瞳孔区域中搜索内边界点,采用三次B样条曲线精确拟合内边界;最后利用Canny算子检测外边界,并采用圆曲线的最小二乘拟合外边界.运用Bath大学虹膜库中的1000幅虹膜图像对该定位算法进行测试,内边界定位时间0.0203s、准确率99.2％;外边界定位时间2.0277s,准确率98.9％,满足准确、高效的定位要求.     

Neural network based biometric personal identification with fast iris segmentation

This paper presents the iris recognition system for biometric personal identification using neural network. Personal identification consists of localization of the iris region and generation of a data set of iris images followed by iris pattern recognition. In this paper, a fast algorithm is proposed for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from an eye image, and, after normalization and enhancement, it is represented by a data set. Using this data set a Neural Network (NN) is used for the classification of iris patterns. The adaptive learning strategy is applied for training of the NN. The results of simulations illustrate the effectiveness of the neural system in personal identification. Recommended by Guest Editor Phill Kyu Rhee. This work was supported by the Near East University. The authors would like to thank Institute of Automation, Chinese Academy of Sciences for providing CASIA iris database. Rahib Hidayat Abiyev was born in Azerbaijan, in 1966. He received the Ph.D. degree in Electrical and Electronic Engineering from Azerbaijan State Oil Academy (old USSR) in 1997. He worked as a Research Assistant at the research laboratory “Industrial intellectual control systems” of Computer-aided control system department. From 1999-present he is working as an Associate Professor at the department of Computer Engineering of Near East University. He is the Chairman of Computer Engineering Department. His research interests are softcomputing, pattern recognition, control systems, signal processing, optimization. Koray Altunkaya was born in Turkey, in 1982. He received the MSc. degree in Computer Engineering from Near East University, North Cyprus in 2007. He is working as an Research Assistant at the research laboratory “Applied Computational Intelligence” of Computer Engineering Department. His research interests are image processing, neural networks, pattern recognition, digital signal processing.     

A new iris segmentation method for non-ideal iris images

Many researchers have studied iris recognition techniques in unconstrained environments, where the probability of acquiring non-ideal iris images is very high due to off-angles, 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 with iris images which are captured in a closely controlled environment. This paper proposes a new iris segmentation method that can be used to accurately extract iris regions from non-ideal quality iris images. This research has following three novelties compared to previous works; firstly, the proposed method uses AdaBoost eye detection in order to compensate for the iris detection error caused by the two circular edge detection operations; secondly, it uses a color segmentation technique for detecting obstructions by the ghosting effects of visible light; and thirdly, if there is no extracted corneal specular reflection in the detected pupil and iris regions, the captured iris image is determined as a “closed eye” image.     

一种改进的虹膜图像分割算法

如何迅速、准确地分割虹膜区域是基于虹膜图像的身份鉴别技术的一个研究热点和难点。本文结合虹膜图像的特点，在内边缘的定位中采用了阈值化结合最小二乘估计的方法；对外边缘和眼睑的边缘图进行预处理，并改进了Hough变换的决策方法，准确而快速地分割出虹膜区域。     

Spiking neural network and wavelets for hiding iris data in digital images

This paper introduces an efficient approach to protect the ownership by hiding the iris data into a digital image for authentication purposes. The idea is to secretly embed an iris code data into the content of the image, which identifies the owner. Algorithms based on Biologically inspired Spiking Neural Networks, called Pulse Coupled Neural Network (PCNN) are first applied to increase the contrast of the human iris image and adjust the intensity with the median filter. It is followed by the PCNN segmentation algorithm to determine the boundaries of the human iris image by locating the pupillary boundary and limbus boundary of the human iris for further processing. A texture segmentation algorithm for isolating the iris from the human eye in a more accurate and efficient manner is presented. A quad tree wavelet transform is first constructed to extract the texture feature. Then, the Fuzzy c-Means (FCM) algorithm is applied to the quad tree in the coarse-to-fine manner by locating the pupillary boundary (inner) and outer (limbus) boundary for further processing. Then, iris codes (watermark) are extracted that characterizes the underlying texture of the human iris by using wavelet theory. Then, embedding and extracting watermarking methods based on Discrete Wavelet Transform (DWT) to insert and extract the generated iris code are presented. The final process deals with the authentication process. In the authentication process, Hamming distance metric that measure the variation between the recorded iris code and the corresponding extracted one from the watermarked image (Stego image) to test weather the Stego image has been modified or not is presented. Simulation results show the effectiveness and efficiency of the proposed approach.     

虹膜定位算法的研究

虹膜识别系统中的虹膜定位精度和定位速度影响识别系统性能.在分析现有虹膜识别算法的基础上,采用基于Canny思想的边缘检测算子提取虹膜图像边缘信息,结合先验知识在小图像块上进行Hough变换拟合虹膜内外圆.实验结果表明,该定位方法在保证定位精度的同时有效地提高了定位速度.虹膜区域的噪声包括眼睑、睫毛、眼睑阴影和光斑等,在眼睑定位方面提出了边缘检测结合Radon变换分段直线定位去除眼睑噪声的方法,同时采用阈值法去除了睫毛和眼睑阴影对虹膜区域的干扰,并用实验验证了该算法的有效性和准确性.     

计算机辅助虹膜诊断中特征提取方法研究

针对计算机辅助虹膜诊断中虹膜分区以及局部特征提取问题,提出了虹膜区域划分方法及具体分区中特征提取的方法.利用虹膜图像中瞳孔的灰度特征,实现虹膜的快速定位.在虹膜定位的基础上,利用虹膜图谱对左右虹膜图像分别进行特征区域划分,并且对划分后的虹膜图像进行规一化处理.在规一化后的虹膜图像中,针对肠区的病理特点,利用改进的Hough变换,提取了虹膜图像中肠道分区的局部病理特征.实验表明,虹膜图像区域划分方法的可行性.