Sub-optimal solutions to track detection problem using graph theoretic concepts

This paper demonstrates how the problem of tracking targets, which appear as either straight or curved lines in two-dimensional display images (or data images) can be formulated in terms of a directed weighted graph model and how dynamic programming techniques can be efficiently applied to reach an optimal or sub-optimal solution. In general, track detection algorithms providing optimal solutions have good detective ability, but most of them suffer from the inability to detect discontinuous lines or to resolve efficiently pairs of crossing lines. A sub-optimal solution is provided that efficiently overcomes these weaknesses. We focus on modeling the track detection problem in terms of a graph, formulating fast sequential/parallel sub-optimal track detection algorithms and testing them on simulated data in order to show their detective ability. Moreover, we specify the conditions under which sub-optimal algorithms can perform at least as well as their corresponding optimal algorithms. This is significant for the track detection problem where fast, accurate and real-time detection is considered a necessity.     

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.     

An efficient color and texture based iris image retrieval technique

This paper proposes a hierarchical approach to retrieve an iris image efficiently from for a large iris database. This approach is a combination of both iris color and texture. Iris color is used for indexing and texture is used for retrieval of iris images from the indexed iris database. An index which is determined from the iris color is used to filter out the images that are not similar to the query image in color. Further, iris texture features of those filtered images, are used to determine the images which are similar to the query image. The iris color information helps to design an efficient indexing scheme based on color indices. The color indices are computed by averaging the intensity values of all red and blue color pixels. Kd-tree is used for real-time indexing based on color indices. The iris texture features are obtained through Speeded Up Robust Features (SURF) algorithm. These features are used to get the query’s corresponding image at the top best match. The performance of the proposed indexing scheme is compared with two well known iris indexing schemes ( [Mehrotra et al., 2010] and [Puhan and Sudha, 2008]) on UPOL (Dobeš, Machala, Tichavský, & Posp?´šil, 2004) and UBIRIS (Proencca & Alexandre, 2005) iris databases. It is observed that combination of iris color and texture improves the performance of iris recognition system.     

不完全虹膜模式唯一性实验研究*

在虹膜识别中,常见的情况就是虹膜信息的不完整,许多文献已经表明在虹膜信息完全的情况下,虹膜的模式是唯一的,模式之间具有差异性,能够通过它的独立特征数来表示模式之间的可分性。但是实际中,虹膜模式受到的影响导致了虹膜模式的信息不完全,有的甚至只有很少的虹膜区域,究竟在小区域内虹膜是否还具有特征的唯一性,是虹膜识别能否实际应用需要考虑的问题。研究结果表明,部分虹膜区域仍具有相当多的独立特征,但随着虹膜区域的减小,错误拒绝率会增加。     

Reliable algorithm for iris segmentation in eye image

The paper presents a novel algorithm for iris segmentation in eye images taken under visible and near infrared light. The proposed approach consists of the following stages: reflections localization, reflections filling in, iris boundaries localization and eyelids boundaries localization. Here, each of these stages is detailed. Authors’ solution obtained the second rank in the “Noisy Iris Challenge Evaluation – Part I” contest, in which all iris segmentation algorithms submitted to the contest were evaluated and compared.     

虹膜图像自相关模型的研究

利用随机场自相关模型，对虹膜图像的自相关特性进行了深入的研究，把环状虹膜展开为矩形表示，以方便虹膜图像的自相关运算，然后，运用3种辨识方法，对简化的自相关模型进行了研究，实验结果表明，理论自相关序列能够较好地与实际自相关序列符合，这对虹膜图像匹配具有重要的意义。     

Efficient iris segmentation method in unconstrained environments

Recently, iris recognition systems have gained increased attention especially in non-cooperative environments. One of the crucial steps in the iris recognition system is the iris segmentation because it significantly affects the accuracy of the feature extraction and iris matching steps. Traditional iris segmentation methods provide excellent results when iris images are captured using near infrared cameras under ideal imaging conditions, but the accuracy of these algorithms significantly decreases when the iris images are taken in visible wavelength under non-ideal imaging conditions. In this paper, a new algorithm is proposed to segments iris images captured in visible wavelength under unconstrained environments. The proposed algorithm reduces the error percentage even in the presence of types of noise include iris obstructions and specular reflection. The proposed algorithm starts with determining the expected region of the iris using the K-means clustering algorithm. The Circular Hough Transform (CHT) is then employed in order to estimate the iris radius and center. A new efficient algorithm is developed to detect and isolate the upper eyelids. Finally, the non-iris regions are removed. Results of applying the proposed algorithm on UBIRIS iris image databases demonstrate that it improves the segmentation accuracy and time.     

Iris quality assessment and bi-orthogonal wavelet based encoding for recognition

Iris recognition has been demonstrated to be an efficient technology for personal identification. In this work, methods to perform iris encoding using bi-orthogonal wavelets and directional bi-orthogonal filters are proposed and compared. All the iris images are enhanced using the wavelet domain in-band de-noising method. This method is shown to improve the iris segmentation results. A framework to assess the iris image quality based on occlusion, contrast, focus and angular deformation is introduced and used as part of a novel adaptive matching technique based on the assessed iris image quality. Adaptive matching presents improved performance when compared against the Hamming distance method. Four different databases are used to analyze the system performance. The first two databases include popular CASIA and high resolution University of Bath databases. Results obtained for these databases compare with results from the literature, in terms of speed as well as accuracy. The other two databases have challenging off-angle (WVU database) and uncontrolled (Clarkson database) iris images and are used to assess the limits of system performance. Best results are achieved for directional bi-orthogonal filter based encoding technique combined with the adaptive matching method with EER values of 0.07%, 0.15%, 0.81% and 1.29% for the four databases, which reflect highly competent performance and high correlation with the quality of the iris images.     

A novel biorthogonal wavelet network system for off-angle iris recognition

One important category of non-ideal conditions for iris recognition is off-angle iris images. Practically it is very difficult for images to be captured with no offset. It then becomes necessary to account for off angle information in order to maintain robust performance. A biorthogonal wavelet based iris recognition system, previously designed at our lab, is modified and demonstrated to perform off-angle iris recognition. Biorthogonal wavelet network (BWN) are developed and trained for each class. The non-ideal factors are adjusted by repositioning the BWN. To test, along with the real data, synthetic iris images are generated by using affine and geometric transforms of 0°, 10° and 20^° experimentally collected images. The tests were carried out on the experimentally collected off-angle data and synthetically generated data for angles from 0° to 60^° with a resolution of 5^°. This approach is shown to have less constraints than a transformation based iris recognition approach. Iris images off-angle by up to 42^° for synthetic data and up to 45^° for experimental data are successfully recognized.     

虹膜识别中标准化区域分辨率的研究

针对虹膜区域图像大小不同,难以实现虹膜的比较问题,提出了根据虹膜最小内边界周长确定角度分辨率和根据虹膜最小内、外边界半径差确定径向分辨率的方法,将不同分辨的虹膜图像转换为相同分辨率的矩形区域。结果表明,采用该方法得到的标准化维数对于虹膜的识别是有效的。     

Coarse iris classification using box-counting to estimate fractal dimensions

This paper proposes a novel algorithm for the automatic coarse classification of iris images using a box-counting method to estimate the fractal dimensions of the iris. First, the iris image is segmented into sixteen blocks, eight belonging to an upper group and eight to a lower group. We then calculate the fractal dimension value of these image blocks and take the mean value of the fractal dimension as the upper and the lower group fractal dimensions. Finally, all the iris images are classified into four categories in accordance with the upper and the lower group fractal dimensions. This classification method has been tested and evaluated on 872 iris cases, and the proportions of these categories in our database are 5.50%, 38.54%, 21.79%, and 34.17%. The iris images are classified with two algorithms, the double threshold algorithm, which classifies iris images with an accuracy of 94.61%, and the backpropagation algorithm, which is 93.23% accurate. When we allow for the border effect, the double threshold algorithm is 98.28% accurate.     

Correlations between Horn fractions, satisfiability and solver performance for fixed density random 3-CNF instances

An enhanced concept of sub-optimal reverse Horn fraction of a CNF-formula was introduced in [18]. It was shown that this fraction is very useful in effectively (almost) separating 3-colorable random graphs with fixed node-edge density from the non-3-colorable ones. A correlation between this enhanced sub-optimal reverse Horn fraction and satisfiability of random 3-SAT instances with a fixed density was observed. In this paper, we present experimental evidence that this correlation scales to larger-sized instances and that it extends to solver performances as well, both of complete and incomplete solvers. Furthermore, we give a motivation for various phases in the algorithm aHS, establishing the enhanced sub-optimal reverse Horn fraction, and we present clear evidence for the fact that the observed correlations are stronger than correlations between satisfiability and sub-optimal MAXSAT-fractions established similarly to the enhanced sub-optimal reverse Horn fraction. The latter observation is noteworthy because the correlation between satisfiability and the optimal MAXSAT-fraction is obviously 100%. AMS subject classification 90C05, 03B99, 68Q01, 68W01     

虹膜定位

为了提高虹膜定位的速度以及虹膜定位算法的健壮性,提出了一种粗定位与精定位相结合的两步定位法,用以进行虹膜定位,并对现有的虹膜定位算法进行了一些改进,实验结果表明,用两步法进行虹膜定位可以加快定位速度,减少搜索计算的盲目性。     

虹膜图象质量评价的研究

一幅虹膜图象采集后 ,首先要进行质量评价 ,再根据评价结果确定其是否可以用于虹膜识别 .将虹膜图象质量评价分为 3个部分 :虹膜检测、虹膜不可用区域的确定以及虹膜图象清晰度评价 ,即采用模板匹配法在图象中检测可能的虹膜 ,并利用虹膜图象的一些特征来校验其可信度 ;寻找虹膜与眼皮的边界线 ,计算虹膜被眼皮遮盖的面积 ,同时根据虹膜和瞳孔的边界参数来计算位于图象外虹膜面积的大小 ,以确定不可用虹膜部分的大小 ;用灰度差分累加和的方法计算瞳孔边沿的平均高度 ,以判断图象中虹膜的清晰程度 .实验结果表明 ,该方法能够有效地筛选出符合要求的虹膜图象 .     

A highly accurate and computationally efficient approach for unconstrained iris segmentation

Biometric research has experienced significant advances in recent years given the need for more stringent security requirements. More important is the need to overcome the rigid constraints necessitated by the practical implementation of sensible but effective security methods such as iris recognition. An inventive iris acquisition method with less constrained image taking conditions can impose minimal to no constraints on the iris verification and identification process as well as on the subject. Consequently, to provide acceptable measures of accuracy, it is critical for such an iris recognition system to be complemented by a robust iris segmentation approach to overcome various noise effects introduced through image capture under different recording environments and scenarios. This research introduces a robust and fast segmentation approach towards less constrained iris recognition using noisy images contained in the UBIRIS.v2 database (the second version of the UBIRIS noisy iris database). The proposed algorithm consists of five steps, which include: (1) detecting the approximate localization of the eye area of the noisy image captured at the visible wavelength using the extracted sclera area, (2) defining the outer iris boundary which is the boundary between iris and sclera, (3) detecting the upper and lower eyelids, (4) conducting the verification and correction for outer iris boundary detection and (5) detecting the pupil area and eyelashes and providing means for verification of the reliability of the segmentation results. The results demonstrate that the accuracy is estimated as 98% when using 500 randomly selected images from the UBIRIS.v2 partial database, and estimated at ?97%$97\%$ in a “Noisy Iris Challenge Evaluation (NICE.I)” in an international competition that involved 97 participants worldwide, ranking this research group in sixth position. This accuracy is achieved with a processing speed nearing real time.     

基于曲面匹配的虹膜识别方法

提出了一种基于曲面匹配的虹膜识别方法,直接利用2幅虹膜图像对应的像素点灰度进行匹配。该方法主要包括虹膜图像尺寸和灰度范围的归一化、匹配区域的选择、匹配相似度阈值的估计以及对人眼旋转的自动校正。该文选择在眼毛、眼睑通常遮挡不到的一个较小的区域作为虹膜匹配区域,使该方法具有较宽的适应能力。没有特征提取过程,使得该方法具有计算量小的特点。图像之间的匹配过程使得伪造和窃取虹膜编码变得困难。实验表明该方法具有较高的识别率。     

一种基于几何原理的虹膜定位算法

虹膜定位是虹膜识别系统中很重要的组成部分,其中定位的速度及准确率是重要的因素。结合人眼的结构特征设计了虹膜定位算法。算法先定位出瞳孔内任意一点,再用边缘检测模板检测出不共线三点定位出内边缘。算法提出用比较四点两次的定位结果来确定外边缘。对已有的755幅虹膜图片进行定位,准确率达98.15%。并且应用简单的几何原理使得算法的鲁棒性能得到保证。     

基于雨雾分离处理和多尺度网络的图像去雨方法

雨带来的雨条纹和雨雾会降低户外拍摄图像的质量,为了去除雨雾对图像的影响,提出了一种基于雨雾分离处理和多尺度卷积神经网络的图像去雨方法。首先利用导向滤波将雨线和图像细节信息提取到高频层,雨雾和背景信息则分离到低频层;然后构建多尺度卷积神经网络来去除高频层中的雨线,网络中融入多个稠密连接模块以提升特征提取的准确性;其次构建多层特征融合的轻量级去雾网络来去除低频层中的雨雾,采用参数一体化结构避免了估计多个大气散射模型参数导致的次优解;最后再结合处理后的高低频结果还原出清晰图像。在多个合成的雨雾数据集以及真实自然场景图像上进行测试,定性和定量结果表明,提出的方法在去除雨雾影响的同时较好地保留了色彩信息,和近年的算法相比,图像结构相似性约提升了0.02~0.08,图像峰值信噪比约提升了0.2~3.5 dB。     

A visual approach for driver inattention detection

Monitoring driver fatigue, inattention, and lack of sleep is very important in preventing motor vehicles accidents. A visual system for automatic driver vigilance has to address two fundamental problems. First of all, it has to analyze the sequence of images and detect if the driver has his eyes open or closed, and then it has to evaluate the temporal occurrence of eyes open to estimate the driver's visual attention level. In this paper we propose a visual approach that solves both problems. A neural classifier is applied to recognize the eyes in the image, selecting two candidate regions that might contain the eyes by using iris geometrical information and symmetry. The novelty of this work is that the algorithm works on complex images without constraints on the background, skin color segmentation and so on. Several experiments were carried out on images of subjects with different eye colors, some of them wearing glasses, in different light conditions. Tests show robustness with respect to situations such as eyes partially occluded, head rotation and so on. In particular, when applied to images where people have eyes closed the proposed algorithm correctly reveals the absence of eyes. Next, the analysis of the eye occurrence in image sequences is carried out with a probabilistic model to recognize anomalous behaviors such as driver inattention or sleepiness. Image sequences acquired in the laboratory and while people were driving a car were used to test the driver behavior analysis and demonstrate the effectiveness of the whole approach.