基于端点标识的虹膜有效区域提取算法

在虹膜识别中,眼睫毛遮挡会降低虹膜识别准确率。为此,提出一种基于端点标识的虹膜有效区域提取算法。该算法在分析眼睫毛结构特征的基础上,对虹膜外圆内的眼睫毛投影端点进行标识,用一个扇形区域来表示眼睫毛遮挡区域,在虹膜外圆中除去该区域与瞳孔,余下则为虹膜有效区域。实验结果表明,该算法所确定的眼睫毛遮挡区域能包含虹膜外边缘内部所有眼睫毛像素,可以提取“纯净”的虹膜有效区域。     

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.      

保护用户数量信息的安全虹膜识别方案

由于传统密码认证方式的不便,生物特征识别技术凭借其便捷、可靠、安全可溯源等特性脱颖而出。在不同的生物特征识别技术中,虹膜识别已被证明能提供较高的识别性能和稳定性,常被用于一些安全性要求较高的领域(如机密组织的认证管理等)。在这些领域中,合法用户数量信息往往也属于机密信息,是不能泄露的,近年来针对虹膜识别的攻击手段也越加先进,通过获得的数量信息可能推测出更多的其他信息,造成更大的安全隐患。但是现有的安全虹膜识别方案仅考虑满足可撤销性、不可逆性和不可连接性,未考虑保护用户数量信息。本文提出一种保护用户数量信息的安全虹膜识别方案,每个用户通过自身虹膜特征随机选择的结果及系统参数共同决定该用户的注册模板数量,攻击者难以根据服务器中存储的虹膜模板数量推测出合法用户数量。该方案能够有效地与现有的安全虹膜识别方案进行结合。理论分析结果表明,本文方案能够保护合法用户数量信息、保护新增用户数量信息、预防关联攻击、并且除了能够保持原始安全虹膜识别方案的可撤销性和不可连接性之外,还能进一步提升原始安全虹膜识别方案的不可逆性。实验结果表明,攻击者准确猜对合法用户数量信息的概率不足15%,且相对误差以及相对期望误差均超过10%,因此本文方案能有效保护用户数量信息,并且不会对原始安全虹膜识别方案的识别精度的影响造成较大影响,差异在0.55%之内。     

VISA: a multimodal database of face and iris traits

In this paper, a new realistic and challenging Face-Iris multimodal biometric database called VISA database is described. One significant problem associated with the development and evaluation of multimodal biometric systems using face and iris biometric traits is the lack of publicly available multimodal databases that are acquired in an unconstrained environment. Currently, there exist no multimodal databases containing a sufficient number of common subjects involved in both face and iris data acquisition process under different conditions. The VISA database fulfills these requirements and it will be a useful tool for the design and development of new algorithms for developing multimodal biometric systems. The VISA iris images are acquired using the IriShield camera. Face images are captured using mobile device. The corpus of a new VISA database consists of face images that vary in expression, pose and illumination, and presence of occlusion whereas iris images vary in illumination, eye movement, and occlusion. A total of more than 5000 images of 100 subjects are collated and used to form the new database. The key features of the VISA dataset are the wide and diverse population of subjects (age and gender). The VISA database is able to support face and/or iris unimodal or multimodal biometric recognition. Hence, the VISA database is a useful addition for the purpose of research and development of biometric systems based on face and iris biometrics. This paper also describes the baseline results of state-of-the-art methods on the VISA dataset and other popular similar datasets. The VISA database will be made available to the public through https://vtu.ac.in/en/visa-multimodal-face-and-iris-biometrics-database/

     

维吾尔语笔迹鉴别方法研究

笔迹鉴别是通过机器分析手写笔迹风格的差异特征来判断书写人身份的一门科学与技术。就像语音、指纹、虹膜和脸谱等生物特征识别技术一样是一个典型的模式识别问题。笔迹鉴别可分为在线、离线两种。笔迹鉴别方法可以分为两大类：文本依存的方法和文本独立的方法。主要针对离线维吾尔语手写体笔迹鉴别方法展开研究,力求提取笔迹图像的全局特征,以提供更多更有效的鉴别信息,结合维吾尔语自身特点对与文本无关的笔迹鉴别中预处理和特征提取技术进行了细致的研究。     

A multi-biometric iris recognition system based on a deep learning approach

Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris-V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person.     

A new multi-unit iris authentication based on quality assessment and score level fusion for mobile phones

Although iris recognition technology has been reported to be more stable and reliable than other biometric systems, performance can be degraded due to many factors such as small eyes, camera defocusing, eyelash occlusions and specular reflections on the surface of glasses. In this paper, we propose a new multi-unit iris authentication method that uses score level fusion based on a support vector machine (SVM) and a quality assessment method for mobile phones. Compared to previous research, this paper presents the following two contributions. First, we reduced the false rejection rate and improved iris recognition accuracy by using iris quality assessment. Second, if even two iris images were determined to be of bad quality, we captured the iris images again without using a recognition process. If only one iris image among the left and right irises was regarded as a good one, it was used for recognition. However, if both the left and right iris images were good, we performed multi-unit iris recognition using score level fusion based on a SVM. Experimental results showed that the accuracy of the proposed method was superior to previous methods that used only one good iris image or those methods that used conventional fusion methods.     

A study on wrist identification for forensic investigation

Criminal and victim identification based on crime scene images is an important part of forensic investigation. Criminals usually avoid identification by covering their faces and tattoos in the evidence images, which are taken in uncontrolled environments. Existing identification methods, which make use of biometric traits, such as vein, skin mark, height, skin colour, weight, race, etc., are considered for solving this problem. The soft biometric traits, including skin colour, gender, height, weight and race, provide useful information but not distinctive enough. Veins and skin marks are limited to high resolution images and some body sites may neither have enough skin marks nor clear veins. Terrorists and rioters tend to expose their wrists in a gesture of triumph, greeting or salute, while paedophiles usually show them when touching victims. However, wrists were neglected by the biometric community for forensic applications. In this paper, a wrist identification algorithm, which includes skin segmentation, key point localisation , image to template alignment, large feature set extraction, and classification, is proposed. The proposed algorithm is evaluated on NTU-Wrist-Image-Database-v1, which consists of 3945 images from 731 different wrists, including 205 pairs of wrist images collected from the Internet, taken under uneven illuminations with different poses and resolutions. The experimental results show that wrist is a useful clue for criminal and victim identification.     

Topological numbering of features on a mesh

Assume we are given ann ×n binary image containing horizontally convex features; i.e., for each feature, each of its row's pixels form an interval on that row. In this paper we consider the problem of assigning topological numbers to such features, i.e., assign a number to every featuref so that all features to the left off in the image have a smaller number assigned to them. This problem arises in solutions to the stereo matching problem. We present a parallel algorithm to solve the topological numbering problem inO(n) time on ann ×n mesh of processors. The key idea of our solution is to create a tree from which the topological numbers can be obtained even though the tree does not uniquely represent the “to the left of” relationship of the features.     

Leveraging implicit demographic information for face recognition using a multi-expert system

This paper describes a novel biometric architecture to implement unsupervised face recognition across varying demographics. The present proposal deals with ethnicity, gender and age, but the same strategy can be crafted for any mix of soft/hard biometrics, sensors, and/or methods. Our aim is not to explicitly distinguish demographic features of a subject (e.g., male vs. female). We rather aim at implicitly exploiting such information to improve the accuracy of subject identification. The role demographics plays in authentication has been reported by many recent studies. Exploiting demographic information can entail two possible strategies. Both require pre-determination of relevant demographic classes, that drive the choice of the best suited recognizer in a set of ad-hoc trained ones. In the first strategy, a human operator visually classifies demographic features of the subject to recognize, and runs the appropriate “strong” recognizer. In the second one, the identification of the most appropriate “strong” recognizer follows the results obtained from a set of upstream classifiers for soft biometrics. Both solutions are poorly suited to most real world applications, e.g., video - surveillance. Our architecture mediates recognition across different demographics without any pre-determination of demographic features. We still have different “strong” classifiers, each trained on a demographic class. The probe is submitted to all of them at once. A supervisor module estimates reliability of the single responses, and the most reliable result is returned. In this approach, classifier reliability is not a static feature, but it is estimated for each probe. The proposed multiple-expert system provides similar performance to pre-determination of demographics. Experimental results show higher flexibility, efficacy and interoperability. We also focus on interoperability across face datasets by adopting EGA (Ethnicity, Gender and Age) database as a benchmark, which is obtained by combining images from several publicly available face datasets.     

The problem of fair division for a hybrid resource

We propose an elementary solution to the apartment rent division problem. This problem belongs to the class of problems of “fair division,” but differs from its standard setting by “heterogeneity,” i.e., the presence of both a conventional continuous component and a discrete one, a fixed set of rooms. A combinatorial-topological approach to solving this problem in a finite number of steps (each of which requires a survey of all participants), actively used in the literature, allows to obtain an approximate decision only. We propose a fundamentally different setting, based on a priori estimates of each room by the participants and allowing, in principle, to consider various optimization tasks as well. Our approach is particularly relevant in the case of a large number of participants. We also note that the proposed approach allows to find a solution in a number of cases where the combinatorial-topological approach does not work.     

Optimal algorithms for rectangle problems on a mesh-connected computer

In this paper, Mesh-Connected Computer (MCC) algorithms for computing several properties of a set of, possibly intersecting rectangles are presented. Given a set of n iso-oriented rectangles, we describe MCC algorithms for determining the following properties: (i) the area of the logic “OR” of these rectangles (i.e., the area of the region covered by at least one rectangle); (ii) the area of the union of pairwise “AND” of the rectangles (i.e., the area of the region covered by two or more rectangles); (iii) the largest number of rectangles that overlap (this solves the fixed-size rectangle placement problem, i.e., given a set of planar points and a rectangle, find a placement of the rectangle in the plane so that the number of points covered by the rectangle is maximal); (iv) the minimum separation between any pair of a set of nonoverlapping rectangles. All these algorithms can be implemented on a 2√n × 2√n MCC in O(√n) time which is optimal. The algorithms compare favorably with the known sequential algorithms that have O(n log n) time complexity.     

FARO: FAce Recognition Against Occlusions and Expression Variations

Face recognition is widely considered as one of the most promising biometric techniques, allowing high recognition rates without being too intrusive. Many approaches have been presented to solve this special pattern recognition problem, also addressing the challenging cases of face changes, mainly occurring in expression, illumination, or pose. On the other hand, less work can be found in literature that deals with partial occlusions (i.e., sunglasses and scarves). This paper presents face recognition against occlusions and expression variations (FARO) as a new method based on partitioned iterated function systems (PIFSs), which is quite robust with respect to expression changes and partial occlusions. In general, algorithms based on PIFSs compute a map of self-similarities inside the whole input image, searching for correspondences among small square regions. However, traditional algorithms of this kind suffer from local distortions such as occlusions. To overcome such limitation, information extracted by PIFS is made local by working independently on each face component (eyes, nose, and mouth). Distortions introduced by likely occlusions or expression changes are further reduced by means of an ad hoc distance measure. In order to experimentally confirm the robustness of the proposed method to both lighting and expression variations, as well as to occlusions, FARO has been tested using AR-faces database, one of the main benchmarks for the scientific community in this context. A further validation of FARO performances is provided by the experimental results produced on Face Recognition Grand Challenge database.     

Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system

In the last few years, social media systems have experienced a fast growth. The amount of content shared in these systems increases fast, leading users to face the well known “interaction overload” problem, i.e., they are overwhelmed by content, so it becomes difficult to come across interesting items. To overcome this problem, social recommender systems have been recently designed and developed in order to filter content and recommend to users only interesting items. This type of filtering is usually affected by the “over-specialization” problem, which is related to recommendations that are too similar to the items already considered by the users. This paper proposes a friend recommender system that operates in the social bookmarking application domain and is based on behavioral data mining, i.e., on the exploitation of the users activity in a social bookmarking system. Experimental results show how this type of mining is able to produce accurate friend recommendations, allowing users to get to know bookmarked resources that are both novel and serendipitous. Using this approach, the impact of the “interaction overload” and the “over-specialization” problems is strongly reduced.     

Linear quadtrees: A blocking technique for contour filling

Given a linear quadtree forming a region's contour, an algorithm is presented to determine all the pixels 4-connected to the border's elements. The procedure, based on a connectivity technique, associates a two-valued state (“blocked” or “unblocked”) with each node and fills increasingly larger quadrants with black nodes whose state is known to be unblocked. Advantages of the proposed procedure over existing ones are: (i) multiply connected regions can be reconstructed; (ii) the border can be given as a set of either 4- or 8-connected pixels.     

Validating a biometric authentication system: sample size requirements

Authentication systems based on biometric features (e.g., fingerprint impressions, iris scans, human face images, etc.) are increasingly gaining widespread use and popularity. Often, vendors and owners of these commercial biometric systems claim impressive performance that is estimated based on some proprietary data. In such situations, there is a need to independently validate the claimed performance levels. System performance is typically evaluated by collecting biometric templates from n different subjects, and for convenience, acquiring multiple instances of the biometric for each of the n subjects. Very little work has been done in 1) constructing confidence regions based on the ROC curve for validating the claimed performance levels and 2) determining the required number of biometric samples needed to establish confidence regions of prespecified width for the ROC curve. To simplify the analysis that addresses these two problems, several previous studies have assumed that multiple acquisitions of the biometric entity are statistically independent. This assumption is too restrictive and is generally not valid. We have developed a validation technique based on multivariate copula models for correlated biometric acquisitions. Based on the same model, we also determine the minimum number of samples required to achieve confidence bands of desired width for the ROC curve. We illustrate the estimation of the confidence bands as well as the required number of biometric samples using a fingerprint matching system that is applied on samples collected from a small population     

Local intensity variation analysis for iris recognition

As an emerging biometric for human identification, iris recognition has received increasing attention in recent years. This paper makes an attempt to reflect shape information of the iris by analyzing local intensity variations of an iris image. In our framework, a set of one-dimensional (1D) intensity signals is constructed to contain the most important local variations of the original 2D iris image. Gaussian-Hermite moments of such intensity signals reflect to a large extent their various spatial modes and are used as distinguishing features. A resulting high-dimensional feature vector is mapped into a low-dimensional subspace using Fisher linear discriminant, and then the nearest center classifier based on cosine similarity measure is adopted for classification. Extensive experimental results show that the proposed method is effective and encouraging.     

Modeling in the design of a KBS validation system

We describe and illustrate the modeling issues in the design of a system for validation of knowledge based systems (KBSs). the domain of such a validation system is “KBSs and their validation problems.” the basic idea in our solution is the following. Since different KBSs may use different knowledge representation languages, we first represent the target KBS (i.e., the KBS to be validated) in a general formal model of KBS, and then validate it in this form. the advantage of this strategy is that validation problem solving needs only to refer to the common language of the general formal model. We present a set of possible conceptual abstraction levels in such a model, and argue that each level is associated with a related view on validation problems. Since high level characterizations are difficult to abstract from current knowledge representation languages, we consider the formal aspects of modeling mainly at the “lowest” level, the so-called inference primitive level. We illustrate the approach by formalizing a solution for selected modeling issues at this level.     

An effective and fast iris recognition system based on a combined multiscale feature extraction technique

The randomness of iris pattern makes it one of the most reliable biometric traits. On the other hand, the complex iris image structure and the various sources of intra-class variations result in the difficulty of iris representation. Although, a number of iris recognition methods have been proposed, it has been found that several accurate iris recognition algorithms use multiscale techniques, which provide a well-suited representation for iris recognition. In this paper and after a thorough analysis and summarization, a multiscale edge detection approach has been employed as a pre-processing step to efficiently localize the iris followed by a new feature extraction technique which is based on a combination of some multiscale feature extraction techniques. This combination uses special Gabor filters and wavelet maxima components. Finally, a promising feature vector representation using moment invariants is proposed. This has resulted in a compact and efficient feature vector. In addition, a fast matching scheme based on exclusive OR operation to compute bits similarity is proposed where the result experimentation was carryout out using CASIA database. The experimental results have shown that the proposed system yields attractive performances and could be used for personal identification in an efficient and effective manner and comparable to the best iris recognition algorithm found in the current literature.     

Personal authentication using digital retinal images

Traditional authentication (identity verification) systems, used to gain access to a private area in a building or to data stored in a computer, are based on something the user has (an authentication card, a magnetic key) or something the user knows (a password, an identification code). However, emerging technologies allow for more reliable and comfortable user authentication methods, most of them based on biometric parameters. Much work could be found in the literature about biometric-based authentication, using parameters like iris, voice, fingerprints, face characteristics, and others. In this work a novel authentication method is presented and preliminary results are shown. The biometric parameter employed for the authentication is the retinal vessel tree, acquired through retinal digital images, i.e., photographs of the fundus of the eye. It has already been asserted by expert clinicians that the configuration of the retinal vessels is unique for each individual and that it does not vary during his life, so it is a very well-suited identification characteristic. Before the verification process can be executed, a registration step is required to align both the reference image and the picture to be verified. A fast and reliable registration method is used to perform this step, so that the whole authentication process takes about 0.3 s.