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.     

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.     

一种新颖的虹膜图像噪声检测方法*

为提高虹膜识别的正确率,针对虹膜图像中存在着眼睫毛和眼睑这两类较难检测的遮挡噪声,在分析现有检测虹膜噪声算法的优缺点后,提出了一套新颖的虹膜图像噪声检测方法：基于Gabor滤波变换的灰度均值法检测睫毛和利用最小二乘法检测眼睑。实验表明,该算法能有效地检测两种遮挡噪声,准确率分别达到95.10%和96.51%,且等错率（EER）指标与已有算法相比最优,提高了虹膜识别系统的整体性能。     

区域注意力机制引导的双路虹膜补全

目的 虹膜识别是一种稳定可靠的生物识别技术,但虹膜图像的采集过程会受到多种干扰造成图像中虹膜被遮挡,比如光斑遮挡、上下眼皮遮挡等。这些遮挡的存在,一方面会导致虹膜信息缺失,直接影响虹膜识别的准确性,另一方面会影响预处理(如定位、分割)的准确性,间接影响虹膜识别的准确性。为解决上述问题,本文提出区域注意力机制引导的双路虹膜补全网络,通过遮挡区域的像素补齐,可以显著减少被遮挡区域对虹膜图像预处理和识别的影响,进而提升识别性能。方法 使用基于Transformer的编码器和基于卷积神经网络(convolutional neural network, CNN)的编码器提取虹膜特征,通过融合模块将两种不同编码器提取的特征进行交互结合,并利用区域注意力机制分别处理低层和高层特征,最后利用解码器对处理后的特征进行上采样,恢复遮挡区域,生成完整图像。结果 在CASIA(Institute of Automation, Chinese Academy of Sciences)虹膜数据集上对本文方法进行测试。在虹膜识别性能方面,本文方法在固定遮挡大小为64×64像素的情况下,遮挡补全结果的TAR(true...     

利用2D-Gabor滤波器提取纹理方向特征的虹膜识别方法*

目前基于2D-Gabor滤波器的虹膜识别算法主要是使用虹膜的相位信息或能量信息作为特征,但在虹膜可用区域减少时这些算法的识别效果明显下降。虹膜纹理除具有上述特征外,还有很强的方向性。在分析了2D-Gabor滤波器的方向和频率选择性后,提出了一种利用2D-Gabor滤波器提取纹理方向特征的虹膜识别方法。实验表明该方法提取的虹膜纹理方向特征可以在较小区域内提取出足够丰富的可区分性特征,实现高准确性的虹膜识别,说明方向特征是一种有效的虹膜识别特征。     

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。     

基于圆形保持水平集方法的虹膜分割研究

虹膜分割是虹膜识别系统中最重要的环节,其分割的好坏将影响虹膜识别的准确率,而虹膜识别也是最可靠的人体生物终身身份标志之一。因此,提出了基于水平集算法的虹膜分割算法。此算法是利用水平集隐式特点与圆形形状方程显式的特点相融合确保了演化曲线在演化过程中仍保持圆形,利用其思想分割内边缘。引入自适应面积项到形状约束的CV模型中用来约束外边缘。实验结果表明,尽管眼睛睁开有限、眼镜和睫毛及眼睑等遮挡以及成像设备形成图像的角度等问题,此模型仍能取得很好的分割效果。选用区域相互重叠度——DICE作为分割算法的评价指标,由实验数据可知,提出的算法对虹膜分割是有效的。     

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.     

基于组合K近邻与矩阵变换的虹膜识别

研究人眼虹膜识别问题,因实际虹膜内边界并不是标准圆,引起识别精度差,影响有效的特征提取.传统利用圆模板定位的算法存在瞳孔遗留或纹理损失且定位时间长等问题,为提高虹膜定位精度,降低识别时间,提出了一种新的虹膜识别算法.首先对图像进行去除光斑等预处理,将含有虹膜图像的圆环变换为极坐标系下的矩形,在矩形坐标上以点、线检测确定虹膜轮廓,并对EMD提取纹理分布特征,根据比对距离寻找每个待测样本的K个近邻,以简单投票决策输出识别结果.基于CASIA虹膜图像库进行仿真,结果表明,识别率高达99％,并明显降低了识别时间,使虹膜定位可有效提升识别精度.     

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。     

Locating the Visible Part of the Iris with a Texture Classifier with a Support Set

Person identification by the iris is one of the leading technologies in biometric identification. The visible region of the iris has the form of a ring enclosed between the pupil and the sclera partially occluded by eyelids, eyelashes, and flashes. An important problem is to find the non-occluded part, i.e., divide the pixels of the image into two classes: “iris” and “occlusions.” We propose an approach to solving this problem based on distinguishing a support set, i.e., a part of the ring which is free from occlusions with high probability, and subsequently finding all elements that have similar texture features. As the support set, based on experiments we have chosen a sector of the ring with minimal brightness excess. We divide the pixels with a classifier based on a multidimensional Gaussian trained on the support set. Local classification noises are partially removed by morphological postprocessing. Applying this algorithm to construct biometric templates improves recognition.     

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.     

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。     

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.     

Toward Accurate and Fast Iris Segmentation for Iris Biometrics

Iris segmentation is an essential module in iris recognition because it defines the effective image region used for subsequent processing such as feature extraction. Traditional iris segmentation methods often involve an exhaustive search of a large parameter space, which is time consuming and sensitive to noise. To address these problems, this paper presents a novel algorithm for accurate and fast iris segmentation. After efficient reflection removal, an Adaboost-cascade iris detector is first built to extract a rough position of the iris center. Edge points of iris boundaries are then detected, and an elastic model named pulling and pushing is established. Under this model, the center and radius of the circular iris boundaries are iteratively refined in a way driven by the restoring forces of Hooke's law. Furthermore, a smoothing spline-based edge fitting scheme is presented to deal with noncircular iris boundaries. After that, eyelids are localized via edge detection followed by curve fitting. The novelty here is the adoption of a rank filter for noise elimination and a histogram filter for tackling the shape irregularity of eyelids. Finally, eyelashes and shadows are detected via a learned prediction model. This model provides an adaptive threshold for eyelash and shadow detection by analyzing the intensity distributions of different iris regions. Experimental results on three challenging iris image databases demonstrate that the proposed algorithm outperforms state-of-the-art methods in both accuracy and speed.     

灰度分布特征的虹膜定位算法研究

为克服浓密睫毛、眼睑等噪声对虹膜定位算法的影响,提出一种有效的去扰虹膜定位算法。对于虹膜的内边缘,在二值化分离瞳孔区域的基础上,利用形态学中的开运算消除噪声,并用灰度投影法定位瞳孔的圆心、半径。而对于虹膜的外边缘,则采用形态学中的闭运算去除虹膜区丰富的纹理,并设计了一个边缘检测模板,在小范围内搜索虹膜外边界的上下左右4个边界点进而确定虹膜的圆心、半径。对比的仿真实验表明：该算法不仅在计算精度和速度上都有很大提高,而且具有较好的鲁棒性。     

A new segmentation approach for iris recognition based on hand-held capture device

Iris segmentation is an important step for automatic iris recognition. This paper presents a new iris segmentation method for hand-held capture device. We use a geometrical method for pupil detection. The bottom point of pupil is used as the reference point for pupil localization because it is insensitive to pupil dilation and not affected by the top eyelid or eyelashes. To decrease computational cost, the outer (or limbus) boundary of iris is localized based on shrunk image using Hough transform and modified Canny edge detector. The lower part of iris pattern is used for recognition in order to reduce the occlusion by eyelashes and eyelids. Experimental results demonstrate that the proposed method has an encouraging performance.