改进的快速虹膜定位算法

针对虹膜的灰度分布特点,提出了一种粗定位和精定位相结合的虹膜定位算法。首先,通过k-mans聚类算法对图像进行动态阈值分割,分离出瞳孔区域,利用圆的几何特性进行粗定位;然后运用Gauss滤波降低噪声干扰和Canny算子进行边缘检测,结合粗定位的结果,应用Hough变换进行精定位,以快速提取虹膜内外边缘。实验表明,该方法能准确快速地定位虹膜的边界以满足实时性要求。     

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.     

基于灰度投影和Hough变换的快速虹膜定位方法

针对虹膜边缘图像提取的困难,提出了将灰度投影和Hough变换相结合的快速定位方法。首先,根据人眼图像的整体灰度分布特征,用灰度投影的方法进行阈值分割,定位出瞳孔,然后对虹膜边缘进行增强操作并提取边缘信息,最后以瞳孔的圆心和半径为参考,缩小搜索范围,用改进的Hough变换法精确定位出虹膜边缘。实验结果表明,该方法提高了虹膜定位的速度,并且具有较好的定位效果。     

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.     

Noisy iris segmentation with boundary regularization and reflections removal

The paper presents an innovative algorithm for the segmentation of the iris in noisy images, with boundaries regularization and the removal of the possible existing reflections. In particular, the method aims to extract the iris pattern from the eye image acquired at the visible wavelength, in an uncontrolled environment where reflections and occlusions can also be present, on-the-move and at variable distance. The method achieves the iris segmentation by the following three main steps. The first step locates the centers of the pupil and the iris in the input image. Then two image strips containing the iris boundaries are extracted and linearizated. The last step locates the iris boundary points in the strips and it performs a regularization operation by achieving the exclusion of the outliers and the interpolation of missing points. The obtained curves are then converted into the original image space in order to produce a first segmentation output. Occlusions such as reflections and eyelashes are then identified and removed from the final area of the segmentation. Results indicate that the presented approach is effective and suitable to deal with the iris acquisition in noisy environments. The proposed algorithm ranked seventh in the international Noisy Iris Challenge Evaluation (NICE.I).     

基于视频眼震图的瞳孔中心定位方法研究

瞳孔定位是计算机视觉领域一个重要的研究课题,涉及到生理学、心理学、人工智能、模式识别、计算机视觉、图像分析与处理等多个学科。对眼睛瞳孔中心的定位是用灰度聚类法粗定位虹膜中心,并用Canny算子进行边缘提取,然后利用Hough变换基于瞳孔的正圆特性精确定位瞳孔中心。     

Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition

This paper describes the winning algorithm we submitted to the recent NICE.I iris recognition contest. Efficient and robust segmentation of noisy iris images is one of the bottlenecks for non-cooperative iris recognition. To address this problem, a novel iris segmentation algorithm is proposed in this paper. After reflection removal, a clustering based coarse iris localization scheme is first performed to extract a rough position of the iris, as well as to identify non-iris regions such as eyelashes and eyebrows. A novel integrodifferential constellation is then constructed for the localization of pupillary and limbic boundaries, which not only accelerates the traditional integrodifferential operator but also enhances its global convergence. After that, a curvature model and a prediction model are learned to deal with eyelids and eyelashes, respectively. Extensive experiments on the challenging UBIRIS iris image databases demonstrate that encouraging accuracy is achieved by the proposed algorithm which is ranked the best performing algorithm in the recent open contest on iris recognition (the Noisy Iris Challenge Evaluation, NICE.I).     

Robust and accurate iris segmentation in very noisy iris images

Iris segmentation plays an important role in an accurate iris recognition system. In less constrained environments where iris images are captured at-a-distance and on-the-move, iris segmentation becomes much more difficult due to the effects of significant variation of eye position and size, eyebrows, eyelashes, glasses and contact lenses, and hair, together with illumination changes and varying focus condition. This paper contributes to robust and accurate iris segmentation in very noisy images. Our main contributions are as follows: (1) we propose a limbic boundary localization algorithm that combines K-Means clustering based on the gray-level co-occurrence histogram and an improved Hough transform, and, in possible failures, a complementary method that uses skin information; the best localization between this and the former is selected. (2) An upper eyelid detection approach is presented, which combines a parabolic integro-differential operator and a RANSAC (RANdom SAmple Consensus)-like technique that utilizes edgels detected by a one-dimensional edge detector. (3) A segmentation approach is presented that exploits various techniques and different image information, following the idea of focus of attention, which progressively detects the eye, localizes the limbic and then pupillary boundaries, locates the eyelids and removes the specular highlight.     

Reliable algorithm for iris segmentation in eye image

The paper presents a novel algorithm for iris segmentation in eye images taken under visible and near infrared light. The proposed approach consists of the following stages: reflections localization, reflections filling in, iris boundaries localization and eyelids boundaries localization. Here, each of these stages is detailed. Authors’ solution obtained the second rank in the “Noisy Iris Challenge Evaluation – Part I” contest, in which all iris segmentation algorithms submitted to the contest were evaluated and compared.     

一种虹膜定位的新算法

针对虹膜外径边缘图像提取的困难,提出对增强了对比度的虹膜图像进行阈值分割之后用圆检测方法进行虹膜定位的简便而快速的算法.首先,根据虹膜图像的边缘图像用圆检测随机Hough变换方法提取瞳孔的圆心与半径;然后,对用直方图均衡化方法增强了对比度的虹膜图像进行阈值分割,提取分割后的图像的二值边缘图像;最后,利用已经提取的瞳孔的圆周参数等先验知识检测虹膜外径与圆心。实验结果表明,该算法提高了虹膜定位的速度,并且具有较好的健壮性与稳定性。     

结合小波变换和Log-Gabor滤波的虹膜识别方法

提出了一种将小波变换和Log-Gabor滤波结合起来进行虹膜识别的方法:小波分解后的低频子带包含了虹膜图像的主要信息,而Log-Gabor滤波能有效提取图像的纹理信息.将归一化的虹膜图像进行两层小波分解,再取其低频子带进行Log-Gabor滤波并量化生成虹膜模板,采用汉明距进行快速分类.实验结果验证了本算法具有很好的识别率和等错率.     

A low complexity Iris localization algorithm for Iris biometrics

Though there has been a plethora of the iris localization schemes, most of these have not been implemented in the real-time systems due to computational complexity. It is mainly because researchers often use the Integro-differential operator (IDO), circular Hough transform (CHT), active control models (ACM), and/or machine-learning (ML) techniques to mark iris in the human eyeimages. While these schemes exhibit relatively better performance, most of these generally take longer due to complex architecture. To contribute to this concern, authors propose a low-complexity iris localization algorithm that works as follows. First, it suppresses the light/specular reflections and sharp gray level variations in the input eyeimage using an order statistic-filter. Using a coarse-to-fine scheme, it locates potential edges in the gradient eyeimage. Next, corresponding to each edge, the gray-level intensity of a circular region is examined. If a compact region having lowest gray-level intensity is found, then it is declared as pupil and its non-circular boundary is extracted using the 8-connectivity method. Finally, using a coarse-to-fine scheme, it marks two safe-regions in input eyeimage, draws a set of three parallel white radial-segments in each safe-region, gets gradient image via the Canny edge-detector, detects potential points on the edge of limbic (iris outer) boundary using the concept of intersection-point of the radial-segment and limbic boundary. The proposed scheme is validated on the MMU V1.0, MMU 2.0, IITD V1.0, CASIA V1.0, CASIA-IrisV3-Twins, CASIA-IrisV3-Interval, CASIA-IrisV3-Lamp, and UBIRIS V1.0. While exhibiting tolerance to noisy regions (e.g., eyelids), it takes less than a second to mark both iris contours, which is a green signal for its real-time applications.

     

一种基于Morlet小波变换的虹膜识别算法

虹膜识别包括虹膜定位、特征提取以及模式匹配几个步骤。文章提出了基于虹膜灰度梯度分析的新定位算法和基于Morlet小波变换的特征提取算法。首先对沿瞳孔半径方向展开的虹膜图像通过寻找灰度梯度最大值位置的方法进行虹膜定位;然后根据虹膜生理的特点对虹膜图像进行分区,对不同的虹膜区域采用一维和二维Morlet复小波变换相结合的特征提取算法,并用二比特格雷编码来表征提取的虹膜纹理的相位信息;最后通过计算虹膜间的Hamming距进行匹配,最终实现虹膜识别。实验结果表明,与现有算法相比,该算法识别速度快,提取特征的效果好,在实验室身份认证系统中表现出很好的识别效果。     

基于感兴趣区域的快速虹膜定位方法*

为解决虹膜图像受光源影响和二值化边缘提取困难的问题,提出一种新的定位方法。该方法首先对瞳孔进行粗定位;然后在瞳孔粗定位的基础上,合理选择感兴趣区域;其次在此感兴趣区域利用行梯度极值取得虹膜外边缘的二值化图像;最后利用最小二乘法计算出虹膜外边缘的圆心和半径。实验结果表明了该算法的有效性。     

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.     

一种改进的瞳孔中心估计方法

经典的虹膜定位算法主要有基于投票机制的Hough变换算法和Daugman提出的基于微积分的圆形检测算法。本文分别对这两种算法进行了分析和验证,针对这两种算法计算量大的缺点提出了一种改进的瞳孔中心估计方法,以减小定位算法搜索瞳孔中心的范围。与传统的瞳孔中心估计方法相比,本文改进的方法能有效缩小估计中心点与实际中心点的误差范围,具有很强的实用性。     

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.     

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.     

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。     

Measuring the degree of eyestrain caused by watching LCD and PDP devices

In this paper, we propose a new way of measuring the degree of eyestrain caused by watching LCD (Liquid Crystal Display) and PDP (Plasma Display Panel) devices. In the experiments, we used a head-mounted eye capturing device and an illumination setup that was designed to avoid specular reflections caused by glasses or contact lenses. Using the captured eye images, we analyzed the eye blinking and changes of pupil sizes (pupil accommodation), using a real-time image processing algorithm. Then we analyzed the degree of eyestrain based on the calculated blinking rate and the pupil accommodation speed. The proposed method offers five improvements over previous methods. First, we perform a comparative analysis of LCD and PDP devices based on the degree of eyestrain. Second, to analyze the degree of eyestrain, we use quantitative data such as the blinking rate and the pupil accommodation speed. Third, we measure the accurate eye blinking and changes of pupil sizes by using high-resolution and zoomed eye image sequences. Fourth, since the camera and illuminative system are based on a specular reflective model, the proposed method can be used with subjects that wear glasses or contact lenses. Fifth, the proposed method is performed at real-time speed.Experimental results showed that the degree of eyestrain when watching LCD devices was greater than that when watching PDP devices.

Relevance to industry

In the large display industry, LCD and PDP devices have become more and more prevalent. In the past, LCD devices have been compared to PDP devices in terms of factors such as spatial resolution, brightness, contrast levels, etc. To perform more accurate comparisons based on human factor, we are proposing a new way of comparing LCD to PDP devices based on the degree of eyestrain.