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 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.     

Texture synthesis and modified filter bank in contourlets for improved iris recognition

Iris recognition in the presence of eyelash occlusions is a challenging task over the years since it has begun. The active area captured under non-ideal imaging conditions usually suffers from low contrast, poor brightness, blur due to camera or subject’s relative motion and particularly eyelash and eyelid occlusions. Accurate segmentation methods avoid occlusions to some extent but not completely always. In the proposed method, pixel-wise texture synthesis is done on occluded regions which improves the correct recognition rate (CRR). The contourlet transform which is a multiresolution tool, decomposes an image into different scales and directions with the help of pyramidal and directional filter bank (PDFB). A new FIR filter named as SSK filter is proposed by the authors for the PDFB in contourlets to extract apt features of iris such that the CRR is further improved. The performance of the proposed method is checked against CASIA-Iris-Interval (V3), IITD, CASIA-V1 and UBIRIS-V1 iris databases, and from the results obtained, it is proved that the proposed method is very much worthwhile for improved iris recognition even in the presence of eyelash and eyelid occlusions.     

Driver’s eye blinking detection using novel color and texture segmentation algorithms

In this paper we propose a system that measures eye blinking rate and eye closure duration. The system consists of skin-color segmentation, facial features segmentation, iris positioning and blink detection. The proposed skin-segmentation procedure is based on a neural network approximation of a RGB skin-color histogram. This method is robust and adaptive to any skin-color training set. The largest remaining skin-color region among skin-color segmentation results is further segmented into open/closed eyes, lips, nose, eyebrows, and the remaining facial regions using a novel texture segmentation algorithm. The segmentation algorithm classifies pixels according to the highest probability among the estimated facial feature class probability density functions (PDFs). The segmented eye regions are analyzed with the Circular Hough transform with the purpose of finding iris candidates. The finial iris position is selected according to the location of the maximum correlation value obtained from correlation with a predefined mask. The positions of irises and eye states are monitored through time to estimate eye blinking frequency and eye closure duration. The method of the driver drowsiness detection using these parameters is illustrated. The proposed system is tested on CCD and CMOS cameras under different environmental conditions and the experimental results show high system performance.     

基于图切割的图像自动分割方法

针对图切割在图像分割中的研究主要集中于交互式图像分割,提出一种基于图切割,对彩色图像和灰度图像均可进行自动分割的方法。通过对图像进行初始化,建立能量函数的数据项、光滑项,图切割求解及迭代执行等步骤自动实现图像的分割,既无需用户人工交互进行约束,也无需对图像建模及对数据分布进行估计,可以较快速度获得良好的分割结果。通过实验对彩色图像和灰度图像进行自动分割,实验结果验证该方法的有效性。     

基于两段多组件图割的非监督彩色图像分割算法

提出基于两段多组件图割的彩色图像分割算法,以解决因标签过多和噪声导致的过分割和图割算法低效等问题.多组件图割算法分割图像时,把标签相同的区域处理为该标签的多个组件,结合两层高斯金字塔形成两段多组件图割,以减少分割错误和标签数量,提高分割的性能.算法首先提取基于多尺度四元数Gabor滤波的texton纹理特征,并自适应融合颜色特征;然后使用两段多组件图割获取图像的优化分割,其中,为了引导图割优化的方向,在平滑项中引入彩色梯度信息;最后去除分割结果中的弱边界,获得最终的分割结果.实验结果表明,相对于比较算法,新算法的分割性能有明显提升.     

融合区域合并和Graph Cuts的彩色图像分割方法

针对使用Graph Cuts方法对图像进行分割极大影响分割精度这一问题,提出了一种新的融合区域分级合并和Graph Cuts的彩色图像分割算法。该算法首先使用均值漂移算法对图像进行初始分割,将原图像分割为具有较好边界的同质区域;然后,通过计算区域相似度对区域进行分级合并,之后构建精简的加权图,并使用Graph Cuts进行分割。多幅彩色图像的分割实验结果证明,所提算法具有较好的分割效果。     

基于滤波器阵列与图割的彩色纹理分割

图割法因无法体现像素点的纹理区域特性而难以应用于纹理分割。针对该问题,提出一种基于滤波器阵列与图割的彩色纹理分割算法。利用构建的滤波器阵列提取图像的纹理特征,并加入图像的H、S、I分量值组成纹理-色彩特征向量,采用texton直方图作为彩色纹理的统计模型对纹理-色彩特征向量进行统计计算,通过直方图差计算像素点间的纹理相似度,再应用图割法中的规范割准则对彩色纹理进行分割。实验结果证明,该算法具有较高的分割准确性。     

基于最小二乘原理的牛眼虹膜分割方法

近年来,动物虹膜识别有成为一种新的动物标识技术的趋势,对于肉类食品安全控制具有潜在的应用价值.提出了一种基于最小二乘原理的牛眼虹膜分割方法,根据牛眼虹膜的结构特征,利用最小二乘原理分别进行内外椭圆拟合.内椭圆利用牛眼瞳孔的边缘直接拟合;外椭圆通过在牛眼虹膜外边缘点中随机选择若干个边缘点来拟合,避免了外边缘点的噪声影响,然后通过计算外边缘点和拟合外椭圆间的距离来寻找最合适的外椭圆;最后通过几何方法进行牛眼虹膜归一化.实验结果表明,该方法与其他传统算法相比,运算速度快,具有较好的拟合精度.     

Color image segmentation based on mean shift and normalized cuts.

In this correspondence, we develop a novel approach that provides effective and robust segmentation of color images. By incorporating the advantages of the mean shift (MS) segmentation and the normalized cut (Ncut) partitioning methods, the proposed method requires low computational complexity and is therefore very feasible for real-time image segmentation processing. It preprocesses an image by using the MS algorithm to form segmented regions that preserve the desirable discontinuity characteristics of the image. The segmented regions are then represented by using the graph structures, and the Ncut method is applied to perform globally optimized clustering. Because the number of the segmented regions is much smaller than that of the image pixels, the proposed method allows a low-dimensional image clustering with significant reduction of the complexity compared to conventional graph-partitioning methods that are directly applied to the image pixels. In addition, the image clustering using the segmented regions, instead of the image pixels, also reduces the sensitivity to noise and results in enhanced image segmentation performance. Furthermore, to avoid some inappropriate partitioning when considering every region as only one graph node, we develop an improved segmentation strategy using multiple child nodes for each region. The superiority of the proposed method is examined and demonstrated through a large number of experiments using color natural scene images.     

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.     

一种改进的基于图割的立体匹配算法

为了提高立体匹配算法的精确度,提出了一种结合图割与Mean Shift图像分割的立体匹配算法。首先用Mean Shift算法对参考图像进行图像分割,得到分割后的标记图,然后将分割信息结合到图割算法的能量函数中,最后用改进的能量函数和图割算法得出致密的视差图。实验结果表明,提出的算法具有更高的精确度和更好的边缘特征。     

全变分流边与M2GGD相结合的自然图像分割方法

提出了一种全变分流边与M2GGD概率密度分布相结合的自然图像分割方法。由于自然图像经常受噪声的污染,导致分割的区域结果视觉效果差,而区域间的边界具有较好的非同质区域区分能力,于是提出了利用全变分流来提取边界,并结合M2GGD概率密度分布构建具有空间约束能力更强的图像分割方法。由于其能量最小化是NP难问题,通过设计最大期望最大似迭代优化方法,将待优化模型的区域项和边缘项,分别转化为多层图割模型的t-link以及n-link,并利用最大流/最小割算法,可求得全局近似最优解。最终,通过在合成的噪声污染图像以及自然场景图像上进行实验对比与分析,实验验证了提出的方法具有较好的抗噪能力,较高的量化准确率,且最终分割的结果接近于地面真实分割结果。     

基于图割的单幅图像影子检测

为了准确检测单幅图像中的影子, 提出一种基于图割的影子检测方法. 首先,使用均值漂移将原始图像分割为若干区域并记录区域之间的边界. 其次,利用支持向量机分类器分别获得分割图像中的候选影子边界和候选影子非影子区域对. 然后,利用候选影子边界两侧的区域信息及候选影子非影子区域对信息构造一个能量函数, 该能量函数反映了将图像中一部分区域划分为影子区域而另一部分区域划分为非影子区域时所需的代价. 再次,结合该能量函数构造出无向图,并证明所构造的无向图的最小割对应能量函数的最小值. 最后,通过图割算法求解该能量函数得到最终的影子检测结果. 实验结果表明,与现有代表最新进展的单幅图像影子检测方法相比,所提方法提高了影子检测结果的准确性和连续性.     

基于端点标识的虹膜有效区域提取算法

在虹膜识别中,眼睫毛遮挡会降低虹膜识别准确率。为此,提出一种基于端点标识的虹膜有效区域提取算法。该算法在分析眼睫毛结构特征的基础上,对虹膜外圆内的眼睫毛投影端点进行标识,用一个扇形区域来表示眼睫毛遮挡区域,在虹膜外圆中除去该区域与瞳孔,余下则为虹膜有效区域。实验结果表明,该算法所确定的眼睫毛遮挡区域能包含虹膜外边缘内部所有眼睫毛像素,可以提取“纯净”的虹膜有效区域。     

Interactively multiphase image segmentation based on variational formulation and graph cuts

In this paper the multiple piecewise constant (MPC) active contour model is extended to deal with multiphase case. This proposed multiphase model can be effectively optimized by solving the minimum cuts problem of a specially devised multilayer graph. Based on the proposed energy functional and its graph cuts optimization, an interactively multiphase partition method for image segmentation is presented. The user places some scribbles with different colors on the image according to the practical application demand and each group of scribbles with the same color corresponds to a potential image region. The distribution of each region can be learned from the input scribbles with some particular color. Then the corresponding multilayer graph can be constructed and its minimum cuts can be computed to determine the segmentation result of the image. Numerical experiments show that the proposed interactively multiphase segmentation method can accurately segment the image into different regions according to the input scribbles with different color.     

Vander Lugt Correlator based active contours for iris segmentation and tracking

Iris segmentation using active contours approaches is receiving increasing attention. In this paper, a self-ruling active contour approach based on the optical correlation algorithm is proposed. The novelty of this research effort is to apply the Optical Correlation based Active Contours (OCAC) on iris segmentation and tracking and highlight the advantages of its small computation time and better accuracy performance. Optical correlation computed with a numerical simulation of the Vander Lugt correlator is used to detect iris and pupil areas which used as an initial contours. As a result, these initial contours assists the method to calculate terms in an energy expression. In the proposed method, several references images called filters of iris and pupil have been introduced. Images from four iris datasets as CASIA v4, WVU non-ideal, MMU2, UBIRIS v2, and a motion video were used in the experiments phase. To present an aggregate overview of the proposed method advantages, we computed several parameters as iris and pupil centers localization errors, iris and pupil rays errors, three performance metrics (as Jaccard coefficient, Dice coefficient, Hausdroff distance), average segmentation error, and average execution time. We compare these segmentation performance parameters with several leading techniques demonstrating significantly improved results with the proposed OCAC technique.     

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.     

基于生物形状知识的人眼定位方法

准确地定位出人眼位置并分离出虹膜、眼睑等区域对虹膜识别、人脸识别等生物特征识别技术具有重要意义.但是,在非理想环境下,人眼图像分辨率通常较低,并且容易受到光照条件、睫毛、阴影等噪声影响,对人眼区域进行正确分割是一项非常具有挑战性的工作.因此,本文针对姿态幅度较小的无遮挡人眼图像分割存在的一些问题,利用Hough圆变换和形态学算法改进低分辨率下人眼的定位.该方法首先利用现有的人脸对齐方法分割出人眼感兴趣区域,采用双线性插值法对人眼图像进行预处理,去除镜面反射光斑;然后根据人眼图像中各区域的灰度分布规律,利用带约束的Hough圆检测算法定位出虹膜;之后结合全局动态阈值、局部自适应阈值及形态学算法分别定位出人眼上下眼睑,并利用最小二乘法拟合上下眼睑,最终分割出人眼虹膜、上下眼睑、巩膜等区域;最后在UBIRIS v1.0数据库及低分辨率人脸图像上对本文提出的算法进行测试.实验结果表明,本文提出的方法对实验室环境下高清虹膜图像及低分辨率人脸图像上的人眼定位均具有较强的鲁棒性.     

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。