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Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on

Issue 3 • Date Aug. 2005

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Displaying Results 1 - 25 of 25
  • Table of contents

    Publication Year: 2005 , Page(s): c1
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  • IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews publication information

    Publication Year: 2005 , Page(s): c2
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  • Guest Editorial Special Issue on Biometric Systems

    Publication Year: 2005 , Page(s): 273 - 275
    Cited by:  Papers (3)
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  • Improved class statistics estimation for sparse data problems in offline signature verification

    Publication Year: 2005 , Page(s): 276 - 286
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (322 KB) |  | HTML iconHTML  

    Sparse data problems are prominent in applications of offline signature verification. By using a small number of training samples, the class statistics estimation errors may be significant, resulting in worsened verification performance. In this paper, we propose two methods to improve the statistics estimation. The first approach employs an elastic distortion model to artificially generate additional training samples for pairs of genuine signatures. These additional samples, together with original genuine samples, are used to compute statistic parameters for a Mahalanobis distance threshold classifier. The other approach is to adopt regularization techniques to overcome the problem of inverting an ill-conditioned sample covariance matrix due to insufficient training samples. A ridge-like estimator is modeled to add some constant values for diagonal elements of the sample covariance matrix. Experimental results showed that both methods were able to improve verification accuracy when they were incorporated with a set of peripheral features. Effectiveness of the methods was validated by quantity analysis. View full abstract»

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  • Fingerprint classification based on learned features

    Publication Year: 2005 , Page(s): 287 - 300
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1107 KB) |  | HTML iconHTML  

    In this paper, we present a fingerprint classification approach based on a novel feature-learning algorithm. Unlike current research for fingerprint classification that generally uses well defined meaningful features, our approach is based on Genetic Programming (GP), which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. Our experimental results show that our approach can find good composite operators to effectively extract useful features. Using a Bayesian classifier, without rejecting any fingerprints from the NIST-4 database, the correct rates for 4- and 5-class classification are 93.3% and 91.6%, respectively, which compare favorably with other published research and are one of the best results published to date. View full abstract»

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  • On the use of different speech representations for speaker modeling

    Publication Year: 2005 , Page(s): 301 - 314
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (484 KB) |  | HTML iconHTML  

    Numerous speech representations have been reported to be useful in speaker recognition. However, there is much less agreement on which speech representation provides a perfect representation of speaker-specific information conveyed in a speech signal. Unlike previous work, we propose an alternative approach to speaker modeling by the simultaneous use of different speech representations in an optimal way. Inspired by our previous empirical studies, we present a soft competition scheme on different speech representations to exploit different speech representations in encoding speaker-specific information. On the basis of this soft competition scheme, we present a parametric statistical model, generalized Gaussian mixture model (GGMM), to characterize a speaker identity based on different speech representations. Moreover, we develop an expectation-maximization algorithm for parameter estimation in the GGMM. The proposed speaker modeling approach has been applied to text-independent speaker recognition and comparative results on the KING speech corpus demonstrate its effectiveness. View full abstract»

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  • Eigenspace-based face recognition: a comparative study of different approaches

    Publication Year: 2005 , Page(s): 315 - 325
    Cited by:  Papers (41)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (413 KB) |  | HTML iconHTML  

    Eigenspace-based face recognition corresponds to one of the most successful methodologies for the computational recognition of faces in digital images. Starting with the Eigenface-Algorithm, different eigenspace-based approaches for the recognition of faces have been proposed. They differ mostly in the kind of projection method used (standard, differential, or kernel eigenspace), in the projection algorithm employed, in the use of simple or differential images before/after projection, and in the similarity matching criterion or classification method employed. The aim of this paper is to present an independent comparative study among some of the main eigenspace-based approaches. We believe that carrying out independent studies is relevant, since comparisons are normally performed using the implementations of the research groups that have proposed each method, which does not consider completely equal working conditions for the algorithms. Very often, a contest between the abilities of the research groups rather than a comparison between methods is performed. This study considers theoretical aspects as well as simulations performed using the Yale Face Database, a database with few classes and several images per class, and FERET, a database with many classes and few images per class. View full abstract»

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  • An appearance model constructed on 3-D surface for robust face recognition against pose and illumination variations

    Publication Year: 2005 , Page(s): 326 - 334
    Cited by:  Papers (5)  |  Patents (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (540 KB) |  | HTML iconHTML  

    We propose a face recognition method that is robust against image variations due to arbitrary lighting and a large extent of pose variations, ranging from frontal to profile views. Existing appearance models defined on image planes are not applicable for such pose variations that cause occlusions and changes of silhouette. In contrast, our method constructs an appearance model of a three-dimensional (3-D) object on its surface. Our proposed model consists of a 3-D shape and geodesic illumination bases (GIBs). GIBs can describe the irradiances of an object's surface under any illumination and generate illumination subspace that can describe illumination variations of an image in an arbitrary pose. Our appearance model is automatically aligned to the target image by pose optimization based on a rough pose, and the residual error of this model fitting is used as the recognition score. We tested the recognition performance of our method with an extensive database that includes 14 000 images of 200 individuals with drastic illumination changes and pose variations up to 60° sideward and 45° upward. The method achieved a first-choice success ratio of 94.2% without knowing precise poses a priori. View full abstract»

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  • Time-series detection of perspiration as a liveness test in fingerprint devices

    Publication Year: 2005 , Page(s): 335 - 343
    Cited by:  Papers (24)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2025 KB) |  | HTML iconHTML  

    Fingerprint scanners may be susceptible to spoofing using artificial materials, or in the worst case, dismembered fingers. An anti-spoofing method based on liveness detection has been developed for use in fingerprint scanners. This method quantifies a specific temporal perspiration pattern present in fingerprints acquired from live claimants. The enhanced perspiration detection algorithm presented here improves our previous work by including other fingerprint scanner technologies; using a larger, more diverse data set; and a shorter time window. Several classification methods were tested in order to separate live and spoof fingerprint images. The dataset included fingerprint images from 33 live subjects, 33 spoofs created with dental material and Play-Doh, and fourteen cadaver fingers. Each method had a different performance with respect to each scanner and time window. However, all the classifiers achieved approximately 90% classification rate for all scanners, using the reduced time window and the more comprehensive training and test sets. View full abstract»

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  • An adaptive multimodal biometric management algorithm

    Publication Year: 2005 , Page(s): 344 - 356
    Cited by:  Papers (25)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (386 KB) |  | HTML iconHTML  

    This paper presents an evolutionary approach to the sensor management of a biometric security system that improves robustness. Multiple biometrics are fused at the decision level to support a system that can meet more challenging and varying accuracy requirements as well as address user needs such as ease of use and universality better than a single biometric system or static multimodal biometric system. The decision fusion rules are adapted to meet the varying system needs by particle swarm optimization, which is an evolutionary algorithm. This paper focuses on the details of this new sensor management algorithm and demonstrates its effectiveness. The evolutionary nature of adaptive, multimodal biometric management (AMBM) allows it to react in pseudoreal time to changing security needs as well as user needs. Error weights are modified to reflect the security and user needs of the system. The AMBM algorithm selects the fusion rule and sensor operating points to optimize system performance in terms of accuracy. View full abstract»

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  • Fingerprint and speaker verification decisions fusion using a functional link network

    Publication Year: 2005 , Page(s): 357 - 370
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (859 KB) |  | HTML iconHTML  

    By exploiting the specialist capabilities of each classifier, a combined classifier may yield results which would not be possible with a single classifier. In this paper, we propose to combine the fingerprint and speaker verification decisions using a functional link network. This is to circumvent the nontrivial trial-and-error and iterative training effort as seen in backpropagation neural networks which cannot guarantee global optimal solutions. In many data fusion applications, as individual classifiers to be combined would have attained a certain level of classification accuracy, the proposed functional link network can be used to combine these classifiers by taking their outputs as the inputs to the network. The proposed network is first applied to a pattern recognition problem to illustrate its approximation capability. The network is then used to combine the fingerprint and speaker verification decisions with much improved receiver operating characteristics performance as compared to several decision fusion methods from the literature. View full abstract»

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  • iShopFloor: an Internet-enabled agent-based intelligent shop floor

    Publication Year: 2005 , Page(s): 371 - 381
    Cited by:  Papers (20)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (632 KB) |  | HTML iconHTML  

    Global competition is driving manufacturing companies to change the way they do business. New kinds of shop floor control systems need to be implemented for these companies to respond quickly to changing shop floor environments and customer demands. This paper presents a new concept called iShopFloor-an intelligent shop floor based on the Internet, web, and agent technologies. It focuses on the implementation of distributed intelligence in the manufacturing shop floor. The proposed approach provides the framework for components of a complex control system to work together as a whole rather than as a disjoint set. It encompasses both information architecture and integration methodologies. The paper introduces the basic concept of iShopFloor, a generic system architecture, and system components. It also describes the implementation of eXtensible Markup Language message services in iShopFloor and the application of intelligent agents to distributed manufacturing scheduling. A prototype environment is presented, and some implementation issues are discussed. View full abstract»

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  • Coupling fuzzy modeling and neural networks for river flood prediction

    Publication Year: 2005 , Page(s): 382 - 390
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (352 KB) |  | HTML iconHTML  

    Over the last decade, neural network-based flood forecast systems have been increasingly used in hydrological research. Usually, input data of the network are composed by past measurements of flows and rainfalls, without providing a description of the saturation state of the basin, which, in contrast, plays a key role in the rainfall-runoff process. This paper couples neural networks and fuzzy logic in order to enrich the description of the basin saturation state for flood forecasting purposes. The basin state is assessed analyzing the total rainfall occurred on a certain time window before the flood event. The proposed framework first classifies the basin saturation state providing a set of fuzzy memberships, and then issues the forecast exploiting a set of neural predictors, each specialized on certain basin saturation condition by means of a weighted least-square training algorithm. The outputs of the specialized neural predictors are linearly weighted, according to the basin state at forecast time: The more the training conditions of a predictor matches the current basin saturation state, the higher its weight on the final forecast. The framework has been tested on an Italian catchment and may overperform classical neural networks approaches. View full abstract»

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  • The life cycle CO2 emission performance of the DOE/NASA solar power satellite system: a comparison of alternative power generation systems in Japan

    Publication Year: 2005 , Page(s): 391 - 400
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (635 KB) |  | HTML iconHTML  

    Solar power generation and, in particular, space solar power generation seem to be one of the most promising electric power generation technologies for reducing emissions of global warming gases (denoted collectively as CO2 emissions below). Calculating the precise amount of net reduction in CO2 emissions of a solar power system over other alternative power systems requires careful life cycle considerations. For example, emissions from a space solar system must include the emissions from consuming rocket fuel during the launching the satellites, and the emissions from the energy consumed while producing the solar panels. In this paper, we calculate the CO2 emissions observed through the life cycle of a solar power satellite (SPS). This life cycle consists of the production of rocket fuel and solar panels and the construction of a Rectenna (power receiving antenna), satellite, and all other equipment listed in the Department of Energy/NASA reference system. The calculation also includes indirect CO2 emissions that occur in various stages of production of these materials. Our baseline scenario shows that the life cycle CO2 emissions for an SPS system per unit of energy generated are almost the same as the emissions for nuclear power systems and are much less than the life cycle emissions for LNG-fired and coal-fired power generation systems. Furthermore, our SPS-Breeder scenario, in which SPSs supply electricity for producing further SPS systems, shows significantly lower CO2 emissions. As electrical power generation constitutes one fourth of Japan's total CO2 emissions, reducing emissions from electric power generation is one of the most important issues on Japan's policy agenda for dealing with global warming. Our findings suggest that the SPS is the most effective alternative power generation technology. View full abstract»

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  • Support vector machines for quality monitoring in a plastic injection molding process

    Publication Year: 2005 , Page(s): 401 - 410
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (796 KB) |  | HTML iconHTML  

    Support vector machines (SVMs) are receiving increased attention in different application domains for which neural networks (NNs) have had a prominent role. However, in quality monitoring little attention has been given to this more recent development encompassing a technique with foundations in statistic learning theory. In this paper, we compare C-SVM and ν-SVM classifiers with radial basis function (RBF) NNs in data sets corresponding to product faults in an industrial environment concerning a plastics injection molding machine. The goal is to monitor in-process data as a means of indicating product quality and to be able to respond quickly to unexpected process disturbances. Our approach based on SVMs exploits the first part of this goal. Model selection which amounts to search in hyperparameter space is performed for study of suitable condition monitoring. In the multiclass problem formulation presented, classification accuracy is reported for both strategies. Experimental results obtained thus far indicate improved generalization with the large margin classifier as well as better performance enhancing the strength and efficacy of the chosen model for the practical case study. View full abstract»

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  • Face authentication from cell phone camera images with illumination and temporal variations

    Publication Year: 2005 , Page(s): 411 - 418
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1340 KB) |  | HTML iconHTML  

    We investigated the performance of three face verification algorithms (correlation filters, Individual PCA, and FisherFaces) on an image database collected by a cell phone camera. Cell phone camera images tend to be of poorer quality along with experiencing scale and dynamic illumination changes due to cell phone portability. While Individual PCA and FisherFaces work in the image domain, correlation filters work in the frequency domain and offer advantages such as shift-invariance, the ability to accommodate in-class image variability, and closed-form expressions. Verification results suggest that, with this database, correlation filters can offer a better performance than Individual PCA and comparable performance with FisherFaces with fewer filters. View full abstract»

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  • Target dependent score normalization techniques and their application to signature verification

    Publication Year: 2005 , Page(s): 418 - 425
    Cited by:  Papers (23)  |  Patents (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (836 KB) |  | HTML iconHTML  

    Score normalization methods in biometric verification, which encompass the more traditional user-dependent decision thresholding techniques, are reviewed from a test hypotheses point of view. These are classified into test dependent and target dependent methods. The focus of the paper is on target dependent score normalization techniques, which are further classified into impostor-centric, target-centric, and target-impostor methods. These are applied to an on-line signature verification system on signature data from the First International Signature Verification Competition (SVC 2004). In particular, a target-centric technique based on the cross-validation procedure provides the best relative performance improvement testing both with skilled (19%) and random forgeries (53%) as compared to the raw verification performance without score normalization (7.14% and 1.06% Equal Error Rate for skilled and random forgeries, respectively). View full abstract»

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  • Hallucinating face by eigentransformation

    Publication Year: 2005 , Page(s): 425 - 434
    Cited by:  Papers (76)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3989 KB) |  | HTML iconHTML  

    In video surveillance, the faces of interest are often of small size. Image resolution is an important factor affecting face recognition by human and computer. In this paper, we propose a new face hallucination method using eigentransformation. Different from most of the proposed methods based on probabilistic models, this method views hallucination as a transformation between different image styles. We use Principal Component Analysis (PCA) to fit the input face image as a linear combination of the low-resolution face images in the training set. The high-resolution image is rendered by replacing the low-resolution training images with high-resolution ones, while retaining the same combination coefficients. Experiments show that the hallucinated face images are not only very helpful for recognition by humans, but also make the automatic recognition procedure easier, since they emphasize the face difference by adding more high-frequency details. View full abstract»

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  • Improving iris recognition accuracy via cascaded classifiers

    Publication Year: 2005 , Page(s): 435 - 441
    Cited by:  Papers (17)  |  Patents (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (954 KB) |  | HTML iconHTML  

    As a reliable approach to human identification, iris recognition has received increasing attention in recent years. The most distinguishing feature of an iris image comes from the fine spatial changes of the image structure. So iris pattern representation must characterize the local intensity variations in iris signals. However, the measurements from minutiae are easily affected by noise, such as occlusions by eyelids and eyelashes, iris localization error, nonlinear iris deformations, etc. This greatly limits the accuracy of iris recognition systems. In this paper, an elastic iris blob matching algorithm is proposed to overcome the limitations of local feature based classifiers (LFC). In addition, in order to recognize various iris images efficiently a novel cascading scheme is proposed to combine the LFC and an iris blob matcher. When the LFC is uncertain of its decision, poor quality iris images are usually involved in intra-class comparison. Then the iris blob matcher is resorted to determine the input iris' identity because it is capable of recognizing noisy images. Extensive experimental results demonstrate that the cascaded classifiers significantly improve the system's accuracy with negligible extra computational cost. View full abstract»

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  • A real-time focusing algorithm for iris recognition camera

    Publication Year: 2005 , Page(s): 441 - 444
    Cited by:  Papers (26)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (374 KB) |  | HTML iconHTML  

    For fast iris recognition, it is very important to capture the user's focused eye image at fast speed. Previous researchers have used the focusing method which has been applied to general landscape scenes without considering the characteristics of the iris image. So, they take much focusing time, especially in the case of the user with glasses. To overcome such problems, we propose a new iris image acquisition method to capture focused eye images at very fast speed based on corneal specular reflection. Experimental results show that the focusing time for both users with and without glasses averages 480 ms, and we conclude that our method can be used for the real-time iris recognition camera. View full abstract»

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  • Model checking for E-business control and assurance

    Publication Year: 2005 , Page(s): 445 - 450
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (127 KB) |  | HTML iconHTML  

    Model checking is a promising technique for the verification of complex software systems. As the use of the Internet for conducting e-business extends the reach of many organizations, well-designed software becomes the foundation of reliable implementation of e-business processes. These distributed, electronic methods of conducting transactions place reliance on the control structures embedded in the transaction processes. Deficiencies in control structures of processes that support e-business can lead to loss of physical assets, digital assets, money, and consumer confidence. Yet, assessing the reliability of e-business processes is complex and time-consuming. This paper explicates how model-checking technology can aid in the design and assurance of e-business processes in complex digital environments. Specifically, we demonstrate how model checking can be used to verify e-business requirements concerning money atomicity, goods atomicity, valid receipt, and communication-link failure. These requirements are fundamental to many e-business applications. Model checking can be used to test a broad range of systems requirements-not only for system designers, but also for auditors and security specialists. Systems that are examined by auditors need to have adequate controls built in prior to implementation and will need adequate auditing after implementation to ensure that none of the processes have been corrupted. Model checkers may also provide value in examining the processes of highly integrated applications as found in enterprise resource planning systems. View full abstract»

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  • Special issue on interdependencies in civil infrastructure systems

    Publication Year: 2005 , Page(s): 451
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  • Special issue on robot learning by observation, demonstration and imitation

    Publication Year: 2005 , Page(s): 452
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  • IEEE Systems, Man, and Cybernetics Society Information

    Publication Year: 2005 , Page(s): c3
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  • IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews Information for authors

    Publication Year: 2005 , Page(s): c4
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Aims & Scope

Overview, tutorial and application papers concerning all areas of interest to the SMC Society: systems engineering, human factors and human machine systems, and cybernetics and computational intelligence. 

Authors should submit human-machine systems papers to the IEEE Transactions on Human-Machine Systems.

Authors should submit systems engineering papers to the IEEE Transactions on Systems, Man and Cybernetics: Systems.

Authors should submit cybernetics papers to the IEEE Transactions on Cybernetics.

Authors should submit social system papers to the IEEE Transactions on Computational Social Systems.

 

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Dr. Vladimir Marik
(until 31 December 2012)