Task-specific gesture analysis in real-time using interpolatedviews

Hand and face gestures are modeled using an appearance-based approach in which patterns are represented as a vector of similarity scores to a set of view models defined in space and time. These view models are learned from examples using unsupervised clustering techniques. A supervised teaming paradigm is then used to interpolate view scores into a task-dependent coordinate system appropriate for recognition and control tasks. We apply this analysis to the problem of context-specific gesture interpolation and recognition, and demonstrate real-time systems which perform these tasks     

A comparison of imputation methods for handling missing scores in biometric fusion

Multibiometric systems, which consolidate or fuse multiple sources of biometric information, typically provide better recognition performance than unimodal systems. While fusion can be accomplished at various levels in a multibiometric system, score-level fusion is commonly used as it offers a good trade-off between data availability and ease of fusion. Most score-level fusion rules assume that the scores pertaining to all the matchers are available prior to fusion. Thus, they are not well equipped to deal with the problem of missing match scores. While there are several techniques for handling missing data in general, the imputation scheme, which replaces missing values with predicted values, is preferred since this scheme can be followed by a standard fusion scheme designed for complete data. In this work, the performance of the following imputation methods are compared in the context of multibiometric fusion: K-nearest neighbor (KNN) schemes, likelihood-based schemes, Bayesian-based schemes and multiple imputation (MI) schemes. Experiments on the MSU database assess the robustness of the schemes in handling missing scores at different missing rates. It is observed that the Gaussian mixture model (GMM)-based KNN imputation scheme results in the best recognition accuracy.     

From scores to face templates: a model-based approach

Regeneration of templates from match scores has security and privacy implications related to any biometric authentication system. We propose a novel paradigm to reconstruct face templates from match scores using a linear approach. It proceeds by first modeling the behavior of the given face recognition algorithm by an affine transformation. The goal of the modeling is to approximate the distances computed by a face recognition algorithm between two faces by distances between points, representing these faces, in an affine space. Given this space, templates from an independent image set (break-in) are matched only once with the enrolled template of the targeted subject and match scores are recorded. These scores are then used to embed the targeted subject in the approximating affine (non-orthogonal) space. Given the coordinates of the targeted subject in the affine space, the original template of the targeted subject is reconstructed using the inverse of the affine transformation. We demonstrate our ideas using three, fundamentally different, face recognition algorithms: Principal Component Analysis (PCA) with Mahalanobis cosine distance measure, Bayesian intra-extrapersonal classifier (BIC), and a feature-based commercial algorithm. To demonstrate the independence of the break-in set with the gallery set, we select face templates from two different databases: Face Recognition Grand Challenge (FRGC) and Facial Recognition Technology (FERET) Database (FERET). With an operational point set at 1 percent False Acceptance Rate (FAR) and 99 percent True Acceptance Rate (TAR) for 1,196 enrollments (FERET gallery), we show that at most 600 attempts (score computations) are required to achieve a 73 percent chance of breaking in as a randomly chosen target subject for the commercial face recognition system. With similar operational set up, we achieve a 72 percent and 100 percent chance of breaking in for the Bayesian and PCA based face recognition systems, respectively. With three different levels of score quantization, we achieve 69 percent, 68 percent and 49 percent probability of break-in, indicating the robustness of our proposed scheme to score quantization. We also show that the proposed reconstruction scheme has 47 percent more probability of breaking in as a randomly chosen target subject for the commercial system as compared to a hill climbing approach with the same number of attempts. Given that the proposed template reconstruction method uses distinct face templates to reconstruct faces, this work exposes a more severe form of vulnerability than a hill climbing kind of attack where incrementally different versions of the same face are used. Also, the ability of the proposed approach to reconstruct actual face templates of the users increases privacy concerns in biometric systems.     

Matching 2.5D face scans to 3D models

The performance of face recognition systems that use two-dimensional images depends on factors such as lighting and subject's pose. We are developing a face recognition system that utilizes three-dimensional shape information to make the system more robust to arbitrary pose and lighting. For each subject, a 3D face model is constructed by integrating several 2.5D face scans which are captured from different views. 2.5D is a simplified 3D (x,y,z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. Two different modalities provided by the facial scan, namely, shape and texture, are utilized and integrated for face matching. The recognition engine consists of two components, surface matching and appearance-based matching. The surface matching component is based on a modified iterative closest point (ICP) algorithm. The candidate list from the gallery used for appearance matching is dynamically generated based on the output of the surface matching component, which reduces the complexity of the appearance-based matching stage. Three-dimensional models in the gallery are used to synthesize new appearance samples with pose and illumination variations and the synthesized face images are used in discriminant subspace analysis. The weighted sum rule is applied to combine the scores given by the two matching components. Experimental results are given for matching a database of 200 3D face models with 598 2.5D independent test scans acquired under different pose and some lighting and expression changes. These results show the feasibility of the proposed matching scheme.     

Modeling and predicting face recognition system performance based on analysis of similarity scores

This paper presents methods of modeling and predicting face recognition (FR) system performance based on analysis of similarity scores. We define the performance of an FR system as its recognition accuracy, and consider the intrinsic and extrinsic factors affecting its performance. The intrinsic factors of an FR system include the gallery images, the FR algorithm, and the tuning parameters. The extrinsic factors include mainly query image conditions. For performance modeling, we propose the concept of "perfect recognition", based on which a performance metric is extracted from perfect recognition similarity scores (PRSS) to relate the performance of an FR system to its intrinsic factors. The PRSS performance metric allows tuning FR algorithm parameters offline for near optimal performance. In addition, the performance metric extracted from query images is used to adjust face alignment parameters online for improved performance. For online prediction of the performance of an FR system on query images, features are extracted from the actual recognition similarity scores and their corresponding PRSS. Using such features, we can predict online if an individual query image can be correctly matched by the FR system, based on which we can reduce the incorrect match rates. Experimental results demonstrate that the performance of an FR system can be significantly improved using the presented methods     

Likelihood ratio-based biometric score fusion

Multibiometric systems fuse information from different sources to compensate for the limitations in performance of individual matchers. We propose a framework for the optimal combination of match scores that is based on the likelihood ratio test. The distributions of genuine and impostor match scores are modeled as finite Gaussian mixture model. The proposed fusion approach is general in its ability to handle 1) discrete values in biometric match score distributions, 2) arbitrary scales and distributions of match scores, 3) correlation between the scores of multiple matchers, and 4) sample quality of multiple biometric sources. Experiments on three multibiometric databases indicate that the proposed fusion framework achieves consistently high performance compared to commonly used score fusion techniques based on score transformation and classification.     

Accuracy estimation of link-based similarity measures and its application

Link-based similarity measures play a significant role in many graph based applications. Consequently, measuring node similarity in a graph is a fundamental problem of graph datamining. Personalized pagerank (PPR) and simrank (SR) have emerged as the most popular and influential link-based similarity measures. Recently, a novel link-based similarity measure, penetrating rank (P-Rank), which enriches SR, was proposed. In practice, PPR, SR and P-Rank scores are calculated by iterative methods. As the number of iterations increases so does the overhead of the calculation. The ideal solution is that computing similarity within the minimum number of iterations is sufficient to guarantee a desired accuracy. However, the existing upper bounds are too coarse to be useful in general. Therefore, we focus on designing an accurate and tight upper bounds for PPR, SR, and P-Rank in the paper. Our upper bounds are designed based on the following intuition: the smaller the difference between the two consecutive iteration steps is, the smaller the difference between the theoretical and iterative similarity scores becomes. Furthermore, we demonstrate the effectiveness of our upper bounds in the scenario of top-k similar nodes queries, where our upper bounds helps accelerate the speed of the query. We also run a comprehensive set of experiments on real world data sets to verify the effectiveness and efficiency of our upper bounds.     

Postprocessing of recognized strings using nonstationary Markovianmodels

This paper presents nonstationary Markovian models and their application to recognition of strings of tokens. Domain specific knowledge is brought to bear on the application of recognizing zip codes in the US mailstream by the use of postal directory files. These files provide a wealth of information on the delivery points (mailstops) corresponding to each zip code. This data feeds into the models as n-grams, statistics that are integrated with recognition scores of digit images. An especially interesting facet of the model is its ability to excite and inhibit certain positions in the n-grams leading to the familiar area of Markov random fields. We empirically illustrate the success of Markovian modeling in postprocessing applications of string recognition. We present the recognition accuracy of the different models on a set of 20000 zip codes. The performance is superior to the present system which ignores all contextual information and simply relies on the recognition scores of the digit recognizers     

Information-theoretic bounds on target recognition performancebased on degraded image data

This paper derives bounds on the performance of statistical object recognition systems, wherein an image of a target is observed by a remote sensor. Detection and recognition problems are modeled as composite hypothesis testing problems involving nuisance parameters. We develop information-theoretic performance bounds on target recognition based on statistical models for sensors and data, and examine conditions under which these bounds are tight. In particular, we examine the validity of asymptotic approximations to probability of error in such imaging problems. Problems involving Gaussian, Poisson, and multiplicative noise, and random pixel deletions are considered, as well as least-favorable Gaussian clutter. A sixth application involving compressed sensor image data is considered in some detail. This study provides a systematic and computationally attractive framework for analytically characterizing target recognition performance under complicated, non-Gaussian models and optimizing system parameters     

A new protocol for multi-biometric systems' evaluation maintaining the dependencies between biometric scores

We address the problem of measuring the dependency of multibiometric systems' scores, using Kolmogorov–Smirnov and Mutual Information criteria, and studying the validity of performance evaluation on chimeric persons. On the NIST-BSSR1 database, we formalize a common assumption in the literature: for independent scores, multibiometric systems can be evaluated on “random chimeric” persons. We show that this is not valid for dependent scores and propose a novel protocol for building “cluster-based chimeric” persons maintaining the level of dependency between scores. Finally, we show that performance evaluation for dependent modalities on such persons is equivalent to that obtained on “real” persons.     

实时系统最坏执行时间分析*

实时系统开发过程中必须强调时间的重要性和支持时间的可预报性。最坏执行时间分析与可调度性分析构成了实时系统时间方面操作可信的基础。最坏执行时间分析计算任务执行时间的上界,这些任务的上界用来分配正确的CPU时间给实时任务。最坏执行时间是可调度分析工具的输入,可调度分析决定了一组任务在一个给定的目标系统下是否可调度。对最坏执行时间分析方面的研究进行了综述,给出在这一领域所取得的进展。 还讨论了在最坏执行时间分析方面存在的问题,给出了将来的研究方向。     

Estimating bounds on the reliability of diverse systems

We address the difficult problem of estimating the reliability of multiple-version software. The central issue is the degree of statistical dependence between failures of diverse versions. Previously published models of failure dependence described what behavior could be expected "on average" from a pair of "independently generated" versions. We focus instead on predictions using specific information about a given pair of versions. The concept of "variation of difficulty" between situations to which software may be subject is central to the previous models cited, and it turns out to be central for our question as well. We provide new understanding of various alternative imprecise estimates of system reliability and some results of practical use, especially with diverse systems assembled from pre-existing (e.g., "off-the-shelf") subsystems. System designers, users, and regulators need useful bounds on the probability of system failure. We discuss how to use reliability data about the individual diverse versions to obtain upper bounds and other useful information for decision making. These bounds are greatly affected by how the versions' probabilities of failure vary between subdomains of the demand space or between operating regimes-it is even possible in some cases to demonstrate, before operation, upper bounds that are very close to the true probability of failure of the system-and by the level of detail with which these variations are documented in the data.     

基于模糊模型相似测量的字符无监督分类法

该文提出一种基于模糊模型相似测量的文本分析系统的字符预分类方法 ,用于对字符的无监督分类 ,以提高整个字符识别系统的速度、正确性和鲁棒性 .作者在字符印刷结构归类的基础上 ,采用模板匹配方法将各类字符分别转换成基于一非线性加权相似函数的模糊样板集合 .模糊字符的无监督分类是字符匹配的一种自然范例并发展了加权模糊相似测量的研究 .该文讨论了该模糊模型的特性、模糊样板匹配的规则 ,并用于加快字符分类处理 ,经过字符分类 ,在字符识别时由于只需针对较小的模糊样板集合而变得容易和快速     

Deterministic Learning and Rapid Dynamical Pattern Recognition

Recognition of temporal/dynamical patterns is among the most difficult pattern recognition tasks. In this paper, based on a recent result on deterministic learning theory, a deterministic framework is proposed for rapid recognition of dynamical patterns. First, it is shown that a time-varying dynamical pattern can be effectively represented in a time-invariant and spatially distributed manner through deterministic learning. Second, a definition for characterizing similarity of dynamical patterns is given based on system dynamics inherently within dynamical patterns. Third, a mechanism for rapid recognition of dynamical patterns is presented, by which a test dynamical pattern is recognized as similar to a training dynamical pattern if state synchronization is achieved according to a kind of internal and dynamical matching on system dynamics. The synchronization errors can be taken as the measure of similarity between the test and training patterns. The significance of the paper is that a completely dynamical approach is proposed, in which the problem of dynamical pattern recognition is turned into the stability and convergence of a recognition error system. Simulation studies are included to demonstrate the effectiveness of the proposed approach     

Multibiometric fusion strategy and its applications: A review

The unimodal biometric based system faced several inherent problems like lack of uniqueness, intra-class variation, non-universality, noisy data (presence of dirt on the sensor), restricted degree of freedom, unacceptable error rate, failure-to-enroll and spoofing attack. Multibiometric is one of the best choices to overcome these problems. Multibiometric fusion plays an important role to enhance the overall performance of the system, in which two or more individual biometric are combined together to form a better performance system. The proper use of fusion strategy is very important in the multibiometric system because it can affect the overall performance and accuracy level of the systems. In designing a multibiometric based system we can use various methods and fusion strategies to combine information from multiple sources. This paper is an in-depth study on multibiometric (multimodal, multialgorithm, multi-sample, multi-sensor and multi-instance) fusion strategy and its different applications. In addition, this paper also discusses the different methodology used in a fusion process (Sensor, Feature, Score, Decision, Rank) of multibiometric systems from last three decades and examines the methods used, to explore their successes and failure.     

The stability robustness determination of state space models with real unstructured perturbations

This paper considers the robust stability of a linear time-invariant state space model subject to real parameter perturbations. The problem is to find the distance of a given stable matrix from the set of unstable matrices. A new method, based on the properties of the Kronecker sum and two other composite matrices, is developed to study this problem; this new method makes it possible to distinguish real perturbations from complex ones. Although a procedure to find the exact value of the distance is still not available, some explicit lower bounds on the distance are obtained. The bounds are applicable only for the case of real plant perturbations, and are easy to compute numerically; if the matrix is large in size, an iterative procedure is given to compute the bounds. Various examples including a 46th-order spacecraft system are given to illustrate the results obtained. The examples show that the new bounds obtained can have an arbitrary degree of improvement over previously reported ones. This work has been supported by the Natural Sciences and Engineering Research Council of Canada under Grant No. A4396.     

基于动态模式的转子系统故障诊断

以Jeffcott转子系统基础松动-碰摩耦合故障为例,研宄动态模式的转子系统故障诊断方法.首先,将转子系统正常和故障时的未知系统动态定义为不同的动态模式,对其进行学习,将学到的知识以常数神经网络权值的形式存储,并建立动态模式库;然后将当前被监测转子系统与动态模式库中的动态模式进行比较,根据动态模式的相似性定义,依据最小误差原则快速判断转子系统与已学过的哪种动态模式相似,实现故障的快速检测与分离.仿真结果验证了算法的有效性.     

Further result on reachable set bounding for linear uncertain polytopic systems with interval time-varying delays

The problem of reachable set estimation of linear uncertain polytopic time-varying delay systems subject to bounded peak inputs is studied in this paper. The delays considered in this paper are assumed to be non-differentiable and vary within an interval where the lower and upper bounds are known. Based on the Lyapunov–Krasovskii method and delay decomposition technique, a sufficient condition for the existence of a ball that bounds the reachable set of the system is proposed in terms of matrix inequalities containing only one scalar which can be solved by using an one-dimensional search method and Matlab’s LMI Toolbox and allow us to find the smallest radius. A numerical example is given to illustrate the effectiveness of the proposed result.     

多传感器目标识别的Vague集方法研究

采用Vague集来表达目标特征的模糊测量信息,提出了一种基于Vague集的多传感器目标识别方法。定义两Vague值之间的贴近度,建立了Vague集表达的目标识别数据库模型,利用贴近度矩阵客观地确定了各特征的权重,根据综合贴近度给出目标识别算法。实例分析表明方法的有效性。     

Silhouette-based gait recognition via deterministic learning

In this paper, we present a new silhouette-based gait recognition method via deterministic learning theory, which combines spatio-temporal motion characteristics and physical parameters of a human subject by analyzing shape parameters of the subject?s silhouette contour. It has been validated only in sequences with lateral view, recorded in laboratory conditions. The ratio of the silhouette?s height and width (H–W ratio), the width of the outer contour of the binarized silhouette, the silhouette area and the vertical coordinate of centroid of the outer contour are combined as gait features for recognition. They represent the dynamics of gait motion and can more effectively reflect the tiny variance between different gait patterns. The gait recognition approach consists of two phases: a training phase and a test phase. In the training phase, the gait dynamics underlying different individuals? gaits are locally accurately approximated by radial basis function (RBF) networks via deterministic learning theory. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. In the test phase, a bank of dynamical estimators is constructed for all the training gait patterns. The constant RBF networks obtained from the training phase are embedded in the estimators. By comparing the set of estimators with a test gait pattern, a set of recognition errors are generated, and the average L₁ norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. The test gait pattern similar to one of the training gait patterns can be recognized according to the smallest error principle. Finally, the recognition performance of the proposed algorithm is comparatively illustrated to take into consideration the published gait recognition approaches on the most well-known public gait databases: CASIA, CMU MoBo and TUM GAID.