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.     

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).     

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.     

Robust iris segmentation on uncalibrated noisy images using mathematical morphology

This paper proposes a new approach for fast iris segmentation that relies on the closed nested structures of iris anatomy (the sclera is brighter than the iris, and the iris is brighter than the pupil) and on its polar symmetry. The described method applies mathematical morphology for polar/radial-invariant image filtering and for circular segmentation using shortest paths from generalized grey-level distances. The proposed algorithm obtained good results on the NICE-I contest and showed a very robust behavior, especially when dealing with half-closed eyes, different skin colours/illumination or subjects wearing glasses.     

面向噪声图像的自适应高容量隐写算法

面向雷达、遥感图像等含噪图像,提出了一种利用噪声自适应嵌入的位平面加密算法。首先对不同的噪声图像利用遗传算法自适应找到最佳嵌入阈值,在噪声值较高处使用一种无须密钥的加密算法将两位秘密信息分别交叉嵌入到第一、三位平面中。然后利用BBE(binary-block embedding)算法将标记位数据压缩到第二位平面。最后在第二位平面冗余处用同样的加密算法将剩余秘密信息分别嵌入到第二、四位平面中完成信息隐藏。实验分析表明在满足高容量嵌入的条件下,与其他自适应位平面隐写算法相比有良好的隐蔽性以及较强的鲁棒性,同时能抵抗目前较好的隐写分析算法,保证了数据传送的安全性。     

Fusing the information in visible light and near-infrared images for iris recognition

Automated human identification is a significant issue in real and virtual societies. Iris is a suitable choice for meeting this goal. In this paper, we present an iris recognition system that uses images acquired in both near-infrared and visible lights. These two types of images reveal different textural information of the iris tissue. We demonstrated the necessity to process both VL and NIR images to recognize irides. The proposed system exploits two feature extraction algorithms: one is based on 1D log-Gabor wavelet which gives a detailed representation of the iris region and the other is based on 1D Haar wavelet which represents a coarse model of iris. The Haar wavelet algorithm is proposed in this paper. It makes smaller iris templates than the 1D log-Gabor approach and yet achieves an appropriate recognition rate. We performed the fusion at the match score level and examined the performance of the system in both verification and identification modes. UTIRIS database was used to evaluate the method. The results were compared with other approaches and proved to have better recognition accuracy, while no image enhancement technique is utilized prior to the feature extraction stage. Furthermore, we demonstrated that fusion can compensate the lack of input image information, which can be beneficial in reducing the computation complexity and handling non-cooperative iris images.     

Toward noncooperative iris recognition: a classification approach using multiple signatures

This paper focuses on noncooperative iris recognition, i.e., the capture of iris images at large distances, under less controlled lighting conditions, and without active participation of the subjects. This increases the probability of capturing very heterogeneous images (regarding focus, contrast, or brightness) and with several noise factors (iris obstructions and reflections). Current iris recognition systems are unable to deal with noisy data and substantially increase their error rates, especially the false rejections, in these conditions. We propose an iris classification method that divides the segmented and normalized iris image into six regions, makes an independent feature extraction and comparison for each region, and combines each of the dissimilarity values through a classification rule. Experiments show a substantial decrease, higher than 40 percent, of the false rejection rates in the recognition of noisy iris images     

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.     

Edge-backpropagation for noisy logo recognition

In this paper, we propose a new approach to improve the performance of multilayer perceptrons operating as autoassociators to classify graphical items in presence of spot noise on the image. The improvement is obtained by introducing a weighed norm instead of using the Euclidean norm to measure the input-output accuracy of the neural network. The weights used in the computation depend on the gradient of the image so as to give less importance to uniform colour regions, like the spots. A modified learning algorithm (edge-backpropagation) is derived from the classical backpropagation by considering the new weighed error function. We report a set of experimental results on a database of 134 company logos corrupted by artificial noise which show the effectiveness of the proposed approach.     

快速准确的虹膜定位算法

针对经典虹膜定位算法速度较慢的问题,提出一种基于圆几何特征的粗定位与精定位结合的快速虹膜定位算法.基于行列扫描的方法找到瞳孔内4条弦,利用弦的中垂线结合瞳孔边界阈值定位四对切点,取切点坐标参数的均值为内边缘粗略定位参数,然后利用微积分方法对内边缘进行精确定位;利用内外边缘圆心非常接近的先验知识,缩小微积分方法定位外边缘的搜索范围,从而显著提高了虹膜定位速度.实验结果表明,该算法显著提高了虹膜定位速度且具有较高的定位精度.     

Feature-domain super-resolution for iris recognition

Uncooperative iris identification systems at a distance suffer from poor resolution of the acquired iris images, which significantly degrades iris recognition performance. Super-resolution techniques have been employed to enhance the resolution of iris images and improve the recognition performance. However, most existing super-resolution approaches proposed for the iris biometric super-resolve pixel intensity values, rather than the actual features used for recognition. This paper thoroughly investigates transferring super-resolution of iris images from the intensity domain to the feature domain. By directly super-resolving only the features essential for recognition, and by incorporating domain specific information from iris models, improved recognition performance compared to pixel domain super-resolution can be achieved. A framework for applying super-resolution to nonlinear features in the feature-domain is proposed. Based on this framework, a novel feature-domain super-resolution approach for the iris biometric employing 2D Gabor phase-quadrant features is proposed. The approach is shown to outperform its pixel domain counterpart, as well as other feature domain super-resolution approaches and fusion techniques.     

一种快速而精确的虹膜定位方法

快速虹膜定位是实现虹膜自动识别系统的基础。从虹膜图像特点出发,提出了基于分块统计灰度平均值的方法来确定虹膜内圆圆心和半径。利用不变矩的识别方法,不需要对任何带估参数进行累积计数,就可快速求出外边缘的参数,计算量小,计算时间短。通过对中科院自动化所CASIA虹膜数据库50组图像的虹膜定位测试结果表明,该方法定位准确率达到95%。     

An edge extraction technique for noisy images

Extracting edges from noisy images has an important significance in practical applications which utilize some type of visual input capability. This paper describes a new edge extraction technique specifically developed for noisy images which eliminates the necessity of noise removal preprocessing or postprocessing. The algorithm is based on parallel statistical tests for which indeterminate decisions are allowed. A number of well-chose examples are shown to demonstrate the capabilities of the new algorithm for noisy images as well as noise free images.     

Second-order convolutional networks for iris recognition

Iris recognition in less constrained environments is challenging as the images taken therein contain severe noisy factors. How to represent iris texture for accurate and robust recognition in such environments is still an open issue. Towards addressing this problem, this paper proposes a novel convolutional network (ConvNet) for effective iris texture representation. The key of the proposed ConvNet is an interaction block which computes an affinity matrix among all pairwise high-level features for learning second-order relationships. The interaction block can model relationships of neighboring and long-range features, and is architecture-agnostic, suitable for different deep network architectures. To further improve the robustness of iris representation, we encode the affinity matrix based on ordinal measure. In addition, we develop a mask network corresponding to the feature learning network, which can exclude the noisy factors during iris matching. We perform thorough ablation studies to evaluate the effectiveness of the proposed networks. Experiments have shown that the proposed networks outperform state-of-the-art (SOTA) methods, achieving a false reject rate (FRR) of 5.49%, 10.41% and 5.80% at 10^??6 false accept rate (FAR) on ND-IRIS-0405, CASIA-IrisV4-Thousand and CASIA-IrisV4-Lamp respectively. And the improvements in equal error rates (EERs) are 0.41%, 0.72% and 0.40%, respectively, as compared with the SOTA methods.

     

Hierarchical fusion of multi-spectral face images for improved recognition performance

This paper presents a two level hierarchical fusion of face images captured under visible and infrared light spectrum to improve the performance of face recognition. At image level fusion, two face images from different spectrums are fused using DWT based fusion algorithm. At feature level fusion, the amplitude and phase features are extracted from the fused image using 2D log polar Gabor wavelet. An adaptive SVM learning algorithm intelligently selects either the amplitude or phase features to generate a fused feature set for improved face recognition. The recognition performance is observed under the worst case scenario of using single training images. Experimental results on Equinox face database show that the combination of visible light and short-wave IR spectrum face images yielded the best recognition performance with an equal error rate of 2.86%. The proposed image-feature fusion algorithm also performed better than existing fusion algorithms.     

A machine-vision system for iris recognition

This paper describes a prototype system for personnel verification based on automated iris recognition. The motivation for this endevour stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric measurement. In particular, it is known in the biomedical community that irises are as distinct as fingerprints or patterns of retinal blood vessels. Further, since the iris is an overt body, its appearance is amenable to remote examination with the aid of a machine-vision system. The body of this paper details the design and operation of such a system. Also presented are the results of an empirical study in which the system exhibits flawless performance in the evaluation of 520 iris images.     

面向高性能计算的分布式故障定位框架

针对高性能计算系统中故障定位难度高且实时性差的问题,提出了一种基于消息传递的故障定位框架（MPFL）,包括基于树形拓扑的故障检测（TFD）和故障分析（TFA）算法。首先,在并行作业初始化时,将所有参与计算的节点进行逻辑上的树形划分,生成故障定位树（FLT）,并将故障定位任务分布到节点上;然后,当消息库、操作系统等组件检测到节点异常状态时,基于TFD算法分析作业的FLT结构,根据负载平衡、性能开销等因素选择接收异常状态的节点;最后,节点利用TFA算法对接收到的异常状态进行推理得出故障,TFA算法使用基于规则的事件关联,并基于消息传递设计轻量级的主动探测,将两种方式相结合,提高了故障分析的准确性。实验以模拟节点停机故障为定位目标,并以NPB-FT与NPB-IS为基准测试,在集群上对MPFL框架进行了评估。实验结果表明,MPFL框架在故障定位能力与开销节省方面表现突出。     

Logo localization and recognition in natural images using homographic class graphs

We propose a method for localization and classification of brand logos in natural images. The system has to overcome multiple challenges such as perspective deformations, warping, variations of the shape and colors, occlusions, background variations. To deal with perspective variation, we rely on homography matching between the SIFT keypoints of logo instances of the same class. To address the changes in color, we construct a weighted graph of logo interconnections that is further analyzed to extract potentially multiple instances of the class. The main instance is built by grouping the keypoints of the graph connected logos onto the central image. The secondary instance is needed for color inverted logos and is obtained by inverting the orientation of the main instance. The constructed logo recognition system is tested on two databases (FlickrLogos-32 and BelgaLogos), outperforming state of the art with more than 10 % accuracy.     

虹膜识别的过零检测循环编码方法

研究了虹膜特征提取和编码识别算法。首先对虹膜图像进行预处理获得归一化的虹膜图像,然后对归一化后的虹膜图像进行高斯平滑滤波和小波过零检测,对得到的特征矩阵通过二次循环编码以得到虹膜的特征值序列,最后通过改进的欧氏距离对虹膜进行判别。实验结果表明,识别率可以达到96.3%。该算法运算简单,能够解决虹膜识别中的旋转不变性问题,得到了较好的识别效果。     

Degradation of iris recognition performance due to non-cosmetic prescription contact lenses

Many iris recognition systems operate under the assumption that non-cosmetic contact lenses have no or minimal effect on iris biometrics performance and convenience. In this paper we show results of a study of 12,003 images from 87 contact-lens-wearing subjects and 9697 images from 124 non-contact-lens-wearing subjects. We visually classified the contact lens images into four categories according to the type of lens effects observed in the image. Our results show different degradations in performance for different types of contact lenses. Lenses that produce larger artifacts on the iris yield more degraded performance. This is the first study to document degraded iris biometrics performance with non-cosmetic contact lenses.