虹膜分割中的噪声检测

针对虹膜图像中的眼睑、眼睫毛和光斑等噪声影响虹膜识别性能,提出一种虹膜分割中的噪声检测方法,在数学形态学的基础上,该方法采用边缘检测与最小二乘法相结合的方法拟合确定眼睑遮挡部分,利用阈值法来检测上眼睫毛和光斑.实验结果表明,该方法可准确而有效地在虹膜分割中检测和剔除这些噪声,是一种有效和可行的虹膜图像噪声检测算法.     

用于虹膜识别的眼睑及眼睫毛遮挡检测

分别针对眼睑和眼睫毛遮挡噪声,利用其灰度和形状信息提出了2种灰度形态学检测算法.1)设计弧线形的形态学结构元素,经过灰度开启运算、图像分割和边缘检测,获得眼睑边缘的候选点集,再利用Bézier曲线拟合出眼睑边缘;2)构造交叉形的形态学结构元素,通过灰度开启运算得到直方图具有分段特性的虹膜图像,经二值化检测出眼睫毛.实验结果表明:文中算法能有效地检测2种遮挡噪声,有助于降低虹膜识别系统的等错误率,提高模式的可分性.     

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.     

基于虹膜识别的眼睫毛检测

针对人眼正常张if-下,眼睫毛的遮挡比较严重,为了降低眼睫毛噪声对虹膜识别的影响,提高虹膜的识别率,设计了基于canny 算子的眼睫毛边缘检测算法.该算法基于canny算子检测图像中的眼睫毛;利用虹膜的外圆,提出了用一个扇形区域来表示眼睫毛遮挡区域.除去眼睫毛遮挡区域与瞳孔,虹膜外圆余下的部分即是虹膜的有效区域.实验表明:该算法可以有效地检测眼睫毛.并且所得到的扇形区域包含外圆内部所有的眼睫毛像素,可以有效的提高模式的可分性和虹膜的识别率.     

一种虹膜图像质量评价的方法

虹膜图像质量是影响虹膜识别系统精度的关键。从序列图像中挑选出符合要求的虹膜图像能有效地降低识别系统的拒识率和误识率。这里按照产生虹膜图像奇异的不同情形：失焦、运动模糊、眼睑遮挡及睫毛遮挡,充分利用虹膜自身的纹理分布特点,在算法中引入了评价区域局部化、小波包分解和能量加权等思想,分别设计相应算法加以评判。实验结果表明该评价方案快速有效,其评价结果和人眼主观评价相吻合。     

虹膜钠环检测算法

钠环是计算机辅助虹膜诊断中十分重要的特征之一,它与身体中胆固醇含量的高低有密切的联系. 给出了一种基于HSI 空间的虹膜钠环检测算法,首先将虹膜与巩膜、睫毛及眼睑进行分割,以减少其对钠环检测造成的遮挡影响;其次将环型虹膜图像做矩形展开处理,以减小钠环的检测范围;最后在HSI 空间下,对矩形虹膜图像的色调、饱和度进行分析,通过将H、S 分量进行数据融合完成对钠环的自动检测,实验证明了该算法能够实现对钠环地准确检测     

一种基于形态学的虹膜定位快速算法

针对有眼睑、睫毛、光斑干扰的虹膜图像的定位速度慢且不精确等问题,提出了一种快速精确的虹膜定位算法。利用灰度投影法和形态学实现瞳孔的粗定位,再分别利用Hough算法和圆周差分算法在小范围内对虹膜内外边缘进行精定位,采用改进后的模板检测法和圆周梯度法定位眼睑,运用灰度阈值法结合形态学检测睫毛。实验结果表明,该算法在定位速度和准确率上都达到了非常好的效果。     

一种噪声密度估计的梯度检测滤波算法

针对高密度椒盐噪声污染图像的去噪声问题,提出了一种噪声密度估计的梯度检测滤波算法。算法首先对含噪声图像进行总体噪声密度检测,计算噪声密度p,对于低密度噪声图像(p≤40%),采用3×3窗口改进的梯度检测滤波算法对图像进行滤波,对于高密度噪声污染图像,采用5×5窗口改进的梯度检测滤波算法对图像进行滤波。实验结果表明,文中算法对高密度椒盐噪声污染图像具有较强的去噪声能力和细节保持性能,具有较高的实际应用价值。     

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.     

Radon变换在虹膜去噪中的应用

Radon变换在数学理论上有重要价值,并可用于图像直线特征的提取。本文基于Radon变换的基本原理,针对在虹膜识别中眼睑和睫毛等噪声信息的存在,提出一种运用Radon变换检测直线的方法检测眼睑并将其去除。通过在MATLAB上仿真结果表明,很好的抑制了噪声对虹膜识别的影响,可达到98.86%的识别率,并在保持1.143%较低拒绝率(FRR,false rejec-tion rate)的同时,也使误识率(FAR,false acceptance rate)减小到0%。     

Toward accurate localization and high recognition performance for noisy iris images

Iris recognition plays an important role in biometrics. Until now, many scholars have made different efforts in this field. However, the recognition performances of most proposed methods degrade dramatically when the image contains some noise, which inevitably occurs during image acquisition such as reflection spots, inconsistent illumination, eyelid, eyelash, hair, etc. In this paper, an accurate iris localization and high recognition performance approach for noisy iris images is presented. After filling the reflection spots using the inpainting method which is based on Navier-Stokes (NS) equations, the Probable boundary (Pb) edge detection operator is used to detect pupil edge initially, which can eliminate the interference of inconsistent illumination, eyelid, eyelash and hair. Besides, the accurate circle parameters are obtained in delicately to reduce the input space of Hough transforms. The iris feature code is constructed based on 1D Log-Gabor filter. Our thorough experimental results on the challenging iris image database CASIA-Iris-Thousand achieve an EER of 1.8272 %, which outperforms the state-of-the-art methods.     

Iris recognition using combined support vector machine and Hamming distance approach

Iris based authentication system is essentially a pattern recognition technique that makes use of iris patterns, which are statistically unique, for the purpose of personal identification. In this study, a novel method for recognition of iris patterns is considered by using a combination of support vector machine and Hamming distance. The zigzag collarette area of the iris is selected for iris feature extraction because it captures the most important areas of iris complex pattern and higher recognition rate is achieved. The proposed approach also used parabola detection and trimmed median filter for the purpose of eyelid and eyelash detection & removal, respectively. The proposed method is computationally effective as well as reliable with a recognition rate of 99.91% and 99.88% on CASIA and Chek image database respectively.     

抗噪的移位Hamming距离差虹膜匹配方法

对传统的Hamming距离匹配方法进行研究,提出抗噪的移位Hamming距离差(OHDD)虹膜匹配方法。首先构造单频两方向的奇对称Gabor滤波器组来提取虹膜边缘特征,然后用过零检测的方法进行虹膜编码,最后使用OHDD参数进行匹配。在6个虹膜数据库中,对传统匹配方法与文中的OHDD匹配方法进行对比实验。实验结果表明,在所有虹膜数据库中,OHDD匹配方法的等错率和正确识别率优于传统匹配方法,并且具有较强的抗眼睑睫毛噪声干扰的能力。     

基于二维正交滤波的虹膜识别算法

提出一种针对灰度虹膜图像进行虹膜特征提取及匹配的方法,其利用四元数二维正交Log Gabor小波提取虹膜图像的纹理特征,以滤波后图像的解析信号作为虹膜的特征编码.该方法可以同时表征虹膜纹理多方向上的特征,更加全面地描述了虹膜纹理的特征空间.特征匹配采用类似汉明距的方式,同时以虹膜图像中眼睑、睫毛以及光斑的分布为匹配模版来减少它们的干扰.大量实验的结果表明该方法具有非常优越的识别性能.     

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.     

由粗到精的虹膜图像离焦模糊评价方法

离焦模糊评价在虹膜识别系统中尤为重要。传统的方法是通过频谱分析测量虹膜图像的离焦模糊程度,这类方法容易受到光照变化以及睫毛和眼皮等噪声区域的影响。提出了一种由粗到精的虹膜图像离焦模糊评价方法。第一步,通过频谱分析去除严重模糊的虹膜图像,进行虹膜图像离焦模糊粗分类。第二步,通过方向金字塔分解,提取虹膜图像的质量特征。在人工合成的离焦模糊虹膜图像数据库中,利用径向基神经网络建立起质量特征与质量等级间的对应关系。通过建立起的模型进行实际的虹膜图像离焦模糊等级预测,以及虹膜图像离焦模糊精分类。在Clarkson数据库上的实验结果证明了该方法不仅可以准确区分清晰图像和离焦模糊图像,而且相比于传统的虹膜图像离焦评价方法更接近于人的视觉感知。     

Iris segmentation in non-ideal images using graph cuts

A non-ideal iris image segmentation approach based on graph cuts is presented that uses both the appearance and eye geometry information. A texture measure based on gradients is computed to discriminate between eyelash and non-eyelash regions, combined with image intensity differences between the iris, pupil, and the background (region surrounding the iris) are utilized as cues for segmentation. The texture and intensity distributions for the various regions are learned from histogramming and explicit sampling of the pixels estimated to belong to the corresponding regions. The image is modeled as a Markov Random Field and the energy minimization is achieved via graph cuts to assign each image pixel one of the four possible labels: iris, pupil, background, and eyelash. Furthermore, the iris region is modeled as an ellipse, and the best fitting ellipse to the initial pixel based iris segmentation is computed to further refine the segmented region. As a result, the iris region mask and the parameterized iris shape form the outputs of the proposed approach that allow subsequent iris recognition steps to be performed for the segmented irises. The algorithm is unsupervised and can deal with non-ideality in the iris images due to out-of-plane rotation of the eye, iris occlusion by the eyelids and the eyelashes, multi-modal iris grayscale intensity distribution, and various illumination effects. The proposed segmentation approach is tested on several publicly available non-ideal near infra red (NIR) iris image databases. We compare both the segmentation error and the resulting recognition error with several leading techniques, demonstrating significantly improved results with the proposed technique.