基于多子区域联合的高适应性虹膜识别算法

 由于在采集虹膜图像前,无法预知眼睑、睫毛等噪声对虹膜纹理的干扰程度和不受干扰的可用虹膜区域的位置和大小,这可能会使提取到的特征模板中包含了由噪声引起的不可靠和不稳定特征,使识别的错误率增加.本文提出了多子区域联合的识别方法,将相对不易受干扰的图像区域划分为4个子区域,分别计算两幅图像对应子区域的相似度,动态选择最相似的子区域,将其特征作为判定依据进行分类.克服了之前算法只选择一个固定位置的区域用于特征提取的局限性.采用CASIA虹膜图库进行测试,结果表明:本方法能提高识别准确率、增强算法对采集图像质量要求的适应性,改善了虹膜识别系统的性能.     

基于虹膜图像特征的噪声检测方法

在虹膜识别系统中,采集得到的图像,除了虹膜信息外,一般还会存在睫毛、眼睑及光源的像点等干扰.这会对虹膜的边界定位和特征提取产生影响,并最终导致识别率降低.通过对眼睑、睫毛、光斑噪声检测的研究,根据噪声的特点,提出一种新的快速定位眼睑的方法,并对双阈值检测睫毛的方法进行改进.试验结果表明,该方法能有效去除虹膜区域噪声干扰,提高识别率.     

基于统计特征的非理想虹膜图像分割方法

由于退化条 件的存在,非理想虹膜识别的关键在于正确 分割虹膜区域,这一区域包含能 够用于个体识别的纹理。本文提出了一种基于统计特性的非理想虹膜图像分割方法,包括内 边界定位、外边界定位和眼睑检 测3个阶段。在内边界定位阶段,通过高斯混合(GMM)模型及多弦长均衡策略,实现对瞳 孔及虹膜中心的精确定位;在外边界定 位阶段,利用简化的基于区域信息的曲线演化方法,将其与序统计滤波(OSF)结合,以确保 曲线收敛至虹膜外边界;在 眼睑检测阶段,利用二次曲线对眼睑进行建模。对多个数据库进行实验的结果表明,本 文 方法能够有效克服反光、睫毛和 眼睑遮挡、外边界模糊等不利因素的影响,精确实现了非理想虹膜图像的分割。     

一种高效的睫毛及眼睑检测方法

通过分析归一化虹膜图像中包含睫毛及眼睑的灰度特征,文中提出一种新的虹膜睫毛及眼睑检测方法,能够同时快速检测睫毛和眼睑,算法模型简单,复杂度低.首先对归一化虹膜图像进行中值滤波,然后用滤波后的图像与原图像做差,最后将做差的图像细化并去除伪目标点.通过对不同图库和一些特殊情况下采集到虹膜图像进行检测,证明该方法能够在很好检测睫毛及眼睑的同时,具有检测速度快,检测精度高的优点,克服了传统方法针对睫毛及眼睑建立不同的数学模型而导致复杂度增加,检测速度慢的缺点.     

一种基于直线行扫描的虹膜快速定位算法

为提高虹膜的定位速度,提出一种粗定位与精定位相结合的虹膜快速定位算法.首先,利用阈值对人眼图像进行分割提取瞳孔,对二值化瞳孔区域进行形态学开元算去除瞳孔区域外睫毛等噪声点;然后对瞳孔区域进行直线行扫描提取瞳孔边界点,并利用边界点进行最小二乘拟合粗略定位内边缘;最后利用圆梯度算子对虹膜内外边缘进行精确定位.对CASIA(version 1.0)虹膜数据库中100多幅虹膜图像进行定位实验,所提算法的平均耗费时间为1.38s,圆梯度算子耗时9.8s,Hough变换方法耗时14.3s.定位结果表明文中算法对不同质量的虹膜图像定位速度快,精度高,鲁棒性强.     

虹膜识别中噪声的检测与处理方法

眼睑、睫毛会严重干扰虹膜定位的准确性,同时也会大大降低虹膜识别的性能,是虹膜定位和识别的主要干扰噪声。为了减少这些噪声对虹膜识别的影响,提高虹膜的识别率,本文采用一种计算与水平方向成不同夹角的弧线上的梯度积分的方法来定位上眼睑,采用径向对称检测下眼睑;采用灰度阈值法检测成块的睫毛,采用改进的二维Log-Gabor小波的实部滤波器来检测稀疏的睫毛。实验表明该算法能够有效去除眼睑和睫毛造成的干扰,提高了虹膜识别性能。     

基于多尺度策略的三步虹膜定位算法研究

为了解决虹膜识别技术中有效、快速地定位虹膜图像,针对传统虹膜定位算法的局限性,提出了一种快速多尺度虹膜定位算法.该算法根据瞳孔、虹膜和巩膜的区域特征,应用多尺度策略,实现了瞳孔、虹膜和眼睑三部分的边缘定位.在相同的实验条件下,对不同个体的虹膜图像进行仿真实验,结果表明,本文算法较传统的边缘检测与Hough变换方法节省了定位时间,增强了定位的稳定性,减少了传统虹膜定位算法中的搜索盲目性.     

空间变化离焦模糊红外图像快速复原算法

为了提升空间变化离焦模糊红外图像的图像质量,提出了一种基于图像质量评价的快速复原算法.本文提出的方法首先对模糊图像采用不同点扩散函数对应的截断约束最小二乘法算法进行复原而获得多幅复原图像,并对复原图像进行去振铃;然后对复原图像中每个像素为中心的区域进行图像质量评价,将采用不同参数复原的图像以图像质量评价的结果进行组合以...     

基于梯度积分投影和最大期望算法的人眼定位

针对传统积分投影方法易受眉毛、睫毛、阴影、遮挡及噪声等干扰的问题,提出了一种梯度积分投影与最大期望(EM)算法相结合的人眼精确定位方法,可以在人脸图像中分割出人眼区域,并精确定位人眼位置。首先,采用一种新的梯度算子计算人脸图像的行梯度积分投影粗略定位人眼区域;然后计算人眼区域的列梯度积分投影函数,用EM算法将所得列梯度积分投影函数曲线拟合成两个高斯曲线,并根据高斯曲线精确分割出人眼窗口;最后,利用我们提出的加权质心法在所得人眼窗口中精确定位双眼位置。在YaleB人脸数据库及自采数据库上的实验结果表明,本文方法不易受眉毛及噪声干扰,并能有效克服眼睑和睫毛的遮挡,对不同光照条件及头部姿态都有很好的鲁棒性。     

虹膜图像内外边缘定位算法研究

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,精确的虹膜定位是有效进行虹膜识别的前提,所以一种简洁有效的虹膜定位算法是至关重要的。针对现有的虹膜定位算法的局限性,提出了一种新的虹膜定位算法。首先从虹膜图像的灰度直方图入手,分析虹膜图像的结构及其灰度分布,先用二值化的方法分离出瞳孔,再用数学形态学方法对其进行膨胀和腐蚀运算,然后确定瞳孔的圆心和半径,对内边缘进行精确定位。利用虹膜定位的先验知识,在缩小搜索范围的基础上,再用形态学算法与新的Hough变换相结合来进行虹膜图像外边缘的定位,确定外圆圆心及半径。实验结果证明了该算法的有效性和可行性。     

面向近眼式应用的快速瞳孔中心定位算法

针对Hough圆变换、最小二乘法椭圆拟合瞳孔中心定位算法中运算量大的问题,提出一种基于椭圆外切矩形的快速瞳孔中心定位算法。该算法从人眼图像中通过阈值分割得到瞳孔区域,对瞳孔区域进行边缘像素点的提取,然后利用椭圆外切矩形模型定位出瞳孔中心。通过Matlab对中国科学院自动化研究所公开的虹膜数据库中756张人眼图像进行遍历实验验证,所提算法定位准确率为98.3%,平均用时0.192 s。在同等条件下,与Hough圆变换、最小二乘法椭圆拟合算法进行对比,在保证定位准确率的同时,所提算法平均用时更少。实验结果表明,所提算法能够快速准确地完成瞳孔的中心定位。     

Iris image segmentation based on phase congruency

Iris i mage recognition is a biometric feature recogni-tiontechnology developedin 1990s .Compared with oth-er biometric feature recognition,iris recognition hasmany advantages suchas uniqueness ,highstability,non-invasive,high peculiarity,anti-false and l…     

Efficient segmentation technique for noisy frontal view iris images using Fourier spectral density

In a less constrained capture of iris images to build a high-speed iris recognition system, the design of a robust and fast iris segmentation method is important. In this paper, a new iris segmentation technique based on the Fourier spectral density is proposed for noisy frontal view eye images captured with minimum cooperation from the subjects. The proposed segmentation method is not an iterative technique and it runs in deterministic time. The computational complexity of the proposed method is found to be significantly lower than the existing approaches based on integro-differential operator, Hough transform and active contour. The basic idea underlying the proposed method is to localize the limbic and pupil boundaries using the Fourier spectral density. The performance studies on a recently created iris database, called UBIRIS (Proenca and Alexandre in Lect Notes Comput Sci 3617:970–977, 2005) containing defocused, reflection-contained and eyelid-occluded iris images in visible spectral range, show that the proposed method is much faster than the existing methods and simultaneously achieves good segmentation accuracy.     

Iris Recognition Using Wavelet Features

The traditional iris recognition systems require equal high quality human iris images. A cheap image acquisition system has difficulty in capturing equal high quality iris images. This paper describes a new feature representation method for iris recognition robust to noises. The disc-shaped iris image is first convolved with a low pass filter along the radial direction. Then, the radially smoothed iris image is decomposed in the angular direction using a one-dimensional continuous wavelet transform. Each decomposed one-dimensional waveform is approximated by an optimal piecewise linear curve connecting a small set of node points. The set of node points is used as a feature vector. The optimal approximation procedure reduces the feature vector size while maintaining recognition accuracy. The similarity between two iris images is measured by the normalized cross-correlation coefficients between optimal curves. The similarity between two iris images is estimated using mid-frequency bands. The rotation of one-dimensional signals due to the head tilt is estimated using the lowest frequency component. Experimentally we show the proposed method produces superb performance in iris recognition.     

利用快速加权PCA检测遮挡区域的人脸识别

针对人脸识别中存在遮挡而影响识别性能的问题,提出了一种利用快速加权PCA检测遮挡区域的鲁棒人脸识别算法。首先,利用快速加权PCA检测输入图像的遮挡区域,将其与图库图像的遮挡区域进行比较;然后,利用局部二值模式匹配确定最优权重系数,利用相位相关算法匹配确定遮挡掩码;最后,计算每个测试图像的匹配得分,并利用最近邻分类器完成人脸识别。在FRGC2和UND人脸库上的实验结果表明,本文算法的识别率可高达99.6%,相比其他几种较新的人脸识别算法,本文算法取得了更好的识别性能。     

基于局部分块特征的快速虹膜身份鉴别算法

提出一种新的虹膜身份鉴别算法.首先将灰度虹膜图像等分为若干个子图像,再将每个子图像等分为若干个子区域,将各点梯度之和最大的子区域中心点坐标作为各子图像的特征,各子图像的特征构成了该虹膜图像的特征矩阵,最后通过特征矩阵在空间上直接对准的方法进行匹配识别.给出了子图像和子区域大小的选择方法,同时给出了在人眼自然张开状态下不受遮挡干扰的可用虹膜区域大小对识别效果的影响.克服了之前人为规定子图像和子区域大小和选取固定可用虹膜区域大小提取局部纹理特征所带来的局限性.实验表明:1)人眼自然张开状态下,在虹膜区域超过50%不受遮挡时即可完成识别;2)算法运行速度快且对采集图像时左右各7°以内的旋转失真具有很强的容错能力.     

Iris on the Move: Acquisition of Images for Iris Recognition in Less Constrained Environments

Iris recognition is one of the most powerful techniques for biometric identification ever developed. Commercial systems based on the algorithms developed by John Daugman have been available since 1995 and have been used in a variety of practical applications. However, all currently available systems impose substantial constraints on subject position and motion during the recognition process. These constraints are largely driven by the image acquisition process, rather than the particular pattern-matching algorithm used for the recognition process. In this paper we present results of our efforts to substantially reduce constraints on position and motion by means of a new image acquisition system based on high-resolution cameras, video synchronized strobed illumination, and specularity based image segmentation. We discuss the design tradeoffs we made in developing the system and the performance we have been able to achieve when the image acquisition system is combined with a standard iris recognition algorithm. The Iris on the Move (IOM) system is the first system to enable capture of iris images of sufficient quality for iris recognition while the subject is moving at a normal walking pace through a minimally confining portal     

一种快速的虹膜定位算法

针对虹膜定位存在速度慢,外边缘模糊导致提取困难等问题,提出一种利用灰度直方图增强对比度,粗定位与精定位结合的三步定位算法。首先利用灰度投影粗定位出瞳孔圆心,再根据灰度直方图将图像二值化后,用算子提取边缘,然后通过粗略搜索缩小搜索范围,最后实现精确定位。试验表明,本算法减少了传统方法中搜索的盲目性,解决了外边缘定位效果不佳的问题,对图像质量要求不高,且快速有效,精度高。     

Speeded up robust features for efficient iris recognition

Iris recognition system is one of the biometric systems in which the development is growing rapidly. In this paper, speeded up robust features (SURFs) are used for detecting and describing iris keypoints. For feature matching, simple fusion rules are applied at different levels. Contrast-limited adaptive histogram equalization (CLAHE) is applied on the normalized image and is compared with histogram equalization (HE) and adaptive histogram equalization (AHE). The aim is to find the best enhancement technique with SURF and to verify the necessity of iris image enhancement. The recognition accuracy in each case is calculated. Experimental results demonstrate that CLAHE is a crucial enhancement step for SURF-based iris recognition. More keypoints can be extracted with enhancement using CLAHE compared to HE and AHE. This alleviates the problem of feature loss and increases the recognition accuracy. The accuracies of recognition using left and right iris images are 99 and 99.5 %, respectively. Fusion of local distances and choosing suitable fusion rules affect the recognition accuracy, noticeably. The proposed SURF-based algorithm is compared with scale-invariant feature transform, histogram of oriented gradients, maximally stable extremal regions and DAISY. Results show that the proposed algorithm is robust to different image variations and gives the highest recognition accuracy.     

Unified framework for automated iris segmentation using distantly acquired face images

Remote human identification using iris biometrics has high civilian and surveillance applications and its success requires the development of robust segmentation algorithm to automatically extract the iris region. This paper presents a new iris segmentation framework which can robustly segment the iris images acquired using near infrared or visible illumination. The proposed approach exploits multiple higher order local pixel dependencies to robustly classify the eye region pixels into iris or noniris regions. Face and eye detection modules have been incorporated in the unified framework to automatically provide the localized eye region from facial image for iris segmentation. We develop robust postprocessing operations algorithm to effectively mitigate the noisy pixels caused by the misclassification. Experimental results presented in this paper suggest significant improvement in the average segmentation errors over the previously proposed approaches, i.e., 47.5%, 34.1%, and 32.6% on UBIRIS.v2, FRGC, and CASIA.v4 at-a-distance databases, respectively. The usefulness of the proposed approach is also ascertained from recognition experiments on three different publicly available databases.