基于纹理分布和Gabor滤波器的虹膜识别算法

该算法基干虹膜纹理的分布特点，将虹膜分成若干带，用Gabor滤波器对每带进行滤波编码，用各带Hamming距离的加权和来判决虹膜的匹配。与一些常规的算法相比，该算法充分考虑了不同区域的纹理特征对虹膜匹配贡献不同的特点，能有效地增大虹膜的类间距，利于进一步减少误识率和拒识率。该文给出了对CASIA虹膜库测试的数据，结果表明该算法是有效的和可行的。该算法已在一些场所的认证系统中试用，效果较理想。     

一种新的基于小波过零检测的虹膜识别算法*

虹膜识别被认为是目前最准确可靠的生物特征识别方法。提出了一种新的基于小波过零检测的虹膜识别算法,通过对分离的虹膜纹理采用小波变换来实现特征提取,最后通过Hamming距离完成模式匹配。实验结果表明,该算法识别率很高,可有效地应用于身份鉴别系统中。     

A New Method for Generating an Invariant Iris Private Key Based on the Fuzzy Vault System

Cryptographic systems have been widely used in many information security applications. One main challenge that these systems have faced has been how to protect private keys from attackers. Recently, biometric cryptosystems have been introduced as a reliable way of concealing private keys by using biometric data. A fuzzy vault refers to a biometric cryptosystem that can be used to effectively protect private keys and to release them only when legitimate users enter their biometric data. In biometric systems, a critical problem is storing biometric templates in a database. However, fuzzy vault systems do not need to directly store these templates since they are combined with private keys by using cryptography. Previous fuzzy vault systems were designed by using fingerprint, face, and so on. However, there has been no attempt to implement a fuzzy vault system that used an iris. In biometric applications, it is widely known that an iris can discriminate between persons better than other biometric modalities. In this paper, we propose a reliable fuzzy vault system based on local iris features. We extracted multiple iris features from multiple local regions in a given iris image, and the exact values of the unordered set were then produced using the clustering method. To align the iris templates with the new input iris data, a shift-matching technique was applied. Experimental results showed that 128-bit private keys were securely and robustly generated by using any given iris data without requiring prealignment.      

Evolutionary design of a fuzzy classifier from data

Genetic algorithms show powerful capabilities for automatically designing fuzzy systems from data, but many proposed methods must be subjected to some minimal structure assumptions, such as rule base size. In this paper, we also address the design of fuzzy systems from data. A new evolutionary approach is proposed for deriving a compact fuzzy classification system directly from data without any a priori knowledge or assumptions on the distribution of the data. At the beginning of the algorithm, the fuzzy classifier is empty with no rules in the rule base and no membership functions assigned to fuzzy variables. Then, rules and membership functions are automatically created and optimized in an evolutionary process. To accomplish this, parameters of the variable input spread inference training (VISIT) algorithm are used to code fuzzy systems on the training data set. Therefore, we can derive each individual fuzzy system via the VISIT algorithm, and then search the best one via genetic operations. To evaluate the fuzzy classifier, a fuzzy expert system acts as the fitness function. This fuzzy expert system can effectively evaluate the accuracy and compactness at the same time. In the application section, we consider four benchmark classification problems: the iris data, wine data, Wisconsin breast cancer data, and Pima Indian diabetes data. Comparisons of our method with others in the literature show the effectiveness of the proposed method.     

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.     

一种基于小波过零检测和SVM的虹膜识别方法*

提出了一种新的基于SVM的虹膜识别算法,通过对虹膜纹理采用小波变换来实现特征提取,最后通过SVM完成模式匹配。实验结果表明,该算法识别率高并可有效地应用于虹膜身份鉴别系统中。     

An effective approach for iris recognition using phase-based image matching

This paper presents an efficient algorithm for iris recognition using phase-based image matching --- an image matching technique using phase components in 2D Discrete Fourier Transforms (DFTs) of given images. Experimental evaluation using CASIA iris image databases (ver. 1.0 and ver. 2.0) and Iris Challenge Evaluation (ICE) 2005 database clearly demonstrates that the use of phase components of iris images makes possible to achieve highly accurate iris recognition with a simple matching algorithm. This paper also discusses major implementation issues of our algorithm. In order to reduce the size of iris data and to prevent the visibility of iris images, we introduce the idea of 2D Fourier Phase Code (FPC) for representing iris information. 2D FPC is particularly useful for implementing compact iris recognition devices using state-of-the-art DSP (Digital Signal Processing) technology.     

基于MATLAB GUI 的虹膜识别算法测试平台

虹膜识别是一种重要的利用生物学特征进行的身份识别技术，针对虹膜识别人们已经提出了许多算法，但如何比较、评价各种算法，还没有一个统一的标准和测试平台，为此该文建立了一个虹膜识别算法的MATLAB图形用户界面（GUI），给出了建立虹膜数据库应该考虑的问题。在该平台下，可以对虹膜识别的有关问题进行研究。对同一个虹膜数据库进行实验，可以获得不同算法的性能指标比较。该平台除了提供统一的操作界面进行各种算法比较研究外，还可以允许用户对新算法进行实验研究。结果表明，该平台能够实现各种算法的集中管理，并输出可视实验结果，很方便进行虹膜识别算法的研究和比较。     

Various hybrid methods based on genetic algorithm with fuzzy logic controller

In this paper we propose several efficient hybrid methods based on genetic algorithms and fuzzy logic. The proposed hybridization methods combine a rough search technique, a fuzzy logic controller, and a local search technique. The rough search technique is used to initialize the population of the genetic algorithm (GA), its strategy is to make large jumps in the search space in order to avoid being trapped in local optima. The fuzzy logic controller is applied to dynamically regulate the fine-tuning structure of the genetic algorithm parameters (crossover ratio and mutation ratio). The local search technique is applied to find a better solution in the convergence region after the GA loop or within the GA loop. Five algorithms including one plain GA and four hybrid GAs along with some conventional heuristics are applied to three complex optimization problems. The results are analyzed and the best hybrid algorithm is recommended.     

EyeCerts

In this paper, we propose EyeCerts, a biometric system for the identification of people which achieves offline verification of certified, cryptographically secure documents. An EyeCert is a printed document which certifies the association of content on the document with a biometric feature-a compressed version of a human iris in this work. The system is highly cost-effective since it does not require high complexity, hard-to-replicate printing technologies. Further, the device used to verify an EyeCert is inexpensive, estimated to have approximately the same cost as an off-the-shelf iris-scanning camera. As a central component of the EyeCert system, we present an iris analysis technique that aims to extract and compress the unique features of a given iris with a discrimination criterion using limited storage. The compressed features should be at maximal distance with respect to a reference iris image database. The iris analysis algorithm performs several steps in three main phases: 1) the algorithm detects the human iris by using a new model which is able to compensate for the noise introduced by the surrounding eyelashes and eyelids, 2) it converts the isolated iris using a modified Fourier-Mellin transform into a standard domain where the common radial patterns of the human iris are concisely represented, and 3) it optimally selects, aligns, and near-optimally compresses the most distinctive transform coefficients for each individual user. Using a low-quality imaging system (sub-U.S.$100), a /spl chi//sup 2/ error distribution model, and assuming a fixed false negatives rate of 5%, EyeCert caused false positives at rates better than 10/sup -5/ and as low as 10/sup -30/ for certain users.     

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。     

基于小波尺度搜索的虹膜识别方法

利用小波的带通特性和小波变换检测奇异点的原理提取虹膜特征,并在此基础上提出了一种新的搜索合适小波尺度的虹膜识别算法。将虹膜纹理分成8个分析带,对每个分析带进行连续小波变换,取其中32个尺度下的结果量化编码。然后利用Hamming距离进行模式匹配,对每个分析带通过搜索最小的Hamming距离获得合适的尺度。实验结果表明,算法的识别率可达98.15%,同以前提出的算法相比识别性能进一步提高,可用于大规模身份识别系统。     

Psychology with soft computing: An integrated approach and its applications

Soft computing techniques proved to be successful in many application areas. In this paper we investigate the application in psychopathological field of two well known soft computing techniques, fuzzy logic and genetic algorithms (GAs). The investigation started from a practical need: the creation of a tool for a quick and correct classification of mental retardation level, which is needed to choose the right treatment for rehabilitation and to assure a quality of life that is suitable for the specific patient condition. In order to meet this need we researched an adaptive data mining technique that allows us to build interpretable models for automatic and reliable diagnosis. Our work concerned a genetic fuzzy system (GFS), which integrates a classical GA and the fuzzy C-means (FCM) algorithm. This GFS, called genetic fuzzy C-means (GFCM), is able to select the best subset of features to generate an efficient classifier for diagnostic purposes from a database of examples. Additionally, thanks to an extension of the FCM algorithm, the proposed technique could also handle databases with missing values. The results obtained in a practical application on a real database of patients and comparisons with established techniques showed the efficiency of the integrated algorithm, both in data mining and completion.     

基于多哈希算法的大规模图像快速检索方法

哈希技术被视为最有潜力的相似性搜索方法,其可以用于大规模多媒体数据搜索场合。为了解决在大规模图像情况下,数据检索效率低下的问题,提出了一种基于分段哈希码的倒排索引树结构,该索引结构将哈希码进行分段处理,对每段哈希码维护一个倒排索引树结构,并结合高效的布隆过滤器构建哈希索引结构。为了进一步提高检索准确性,设计了一种准确的排序融合算法,对多个哈希算法的排序结果分别构建加权无向图,采用PageRank的思想对基于多个哈希算法的排序列表的融合技术进行了详细的说明。实验结果表明,基于分段哈希码的倒排索引树结构能极大地提升数据的检索速度。此外,相比于传统的单个哈希算法排序技术,基于多个哈希算法的排序列表融合技术的检索准确率优势显著。     

IrisCode decompression based on the dependence between its bit pairs

IrisCode is an iris recognition algorithm developed in 1993 and continuously improved by Daugman. Understanding IrisCode's properties is extremely important because over 60 million people have been mathematically enrolled by the algorithm. In this paper, IrisCode is proved to be a compression algorithm, which is to say its templates are compressed iris images. In our experiments, the compression ratio of these images is 1:655. An algorithm is designed to perform this decompression by exploiting a graph composed of the bit pairs in IrisCode, prior knowledge from iris image databases, and the theoretical results. To remove artifacts, two postprocessing techniques that carry out optimization in the Fourier domain are developed. Decompressed iris images obtained from two public iris image databases are evaluated by visual comparison, two objective image quality assessment metrics, and eight iris recognition methods. The experimental results show that the decompressed iris images retain iris texture that their quality is roughly equivalent to a JPEG quality factor of 10 and that the iris recognition methods can match the original images with the decompressed images. This paper also discusses the impacts of these theoretical and experimental findings on privacy and security.     

基于虹膜特征密钥的营房信息加密研究

在军队后勤信息化建设中, 营房勤务信息化是重要的组成部分. 为解决营房信息化中信息安全面临的严峻形势和传统加密手段的安全隐患, 提出一种基于虹膜特征密钥的信息加密算法. 从人体虹膜中提取375位虹膜特征码, 然后从特征码中提取加密密钥用于营房信息加密. 实验结果表明, 加密信息的安全性得到了提高.     

基于多通道小波滤波的虹膜识别新算法

为克服小波变换过零检测虹膜识别算法对纹理灰度变化敏感的缺点,利用小波变换多通道滤波的特性,提出了一种新的基于Daubechies-4小波的虹膜识别新算法。根据虹膜纹理分布的特征,将虹膜分成10个分析带,对每个分析带采用一个合适尺度的小波滤波,小波变换各个通道的小波系数的均值及标准差作为虹膜的特征值,最后得到虹膜的128位特征编码。特征匹配采用的是加权欧式距离的分类器方法。实验结果表明算法是有效的,取得了高识别率;同时,提出的算法对虹膜图像的尺度、旋转、平移等的变化具有不变性。     

Object-stack: An object-oriented approach for top-k keyword querying over fuzzy XML

Keyword search is the most popular technique of searching information from XML (eXtensible markup language) document. It enables users to easily access XML data without learning the structure query language or studying the complex data schemas. Existing traditional keyword query methods are mainly based on LCA (lowest common ancestor) semantics, in which the returned results match all keywords at the granularity of elements. In many practical applications, information is often uncertain and vague. As a result, how to identify useful information from fuzzy data is becoming an important research topic. In this paper, we focus on the issue of keyword querying on fuzzy XML data at the granularity of objects. By introducing the concept of “object tree”, we propose the query semantics for keyword query at object-level. We find the minimum whole matching result object trees which contain all keywords and the partial matching result object trees which contain partial keywords, and return the root nodes of these result object trees as query results. For effectively and accurately identifying the top-K answers with the highest scores, we propose a score mechanism with the consideration of tf*idf document relevance, users’ preference and possibilities of results. We propose a stack-based algorithm named object-stack to obtain the top-K answers with the highest scores. Experimental results show that the object-stack algorithm outperforms the traditional XML keyword query algorithms significantly, and it can get high quality of query results with high search efficiency on the fuzzy XML document.     

图数据库中的相似性搜索算法研究与应用*

图数据库的相似性搜索是一个非常重要的研究内容,图的相似性匹配属于图同构的判定问题,是NP完全问题,传统的高开销搜索的方法已经不能满足复杂图查询的需要;另外,由于图数据库的复杂性和特殊性,已有的优化算法不能直接使用。为了提高图数据库的搜索效率,提出了一种基于索引的相似性搜索算法,通过数据库中的频繁结构建立特征索引,算法可高效准确地滤除大量的非相似图集合,避免了图之间精确匹配即图同构的计算,最后将本算法应用于化学数据库,实验结果证明了该方法的有效性和可行性。