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Computational Intelligence Magazine, IEEE

Issue 2 • Date May 2007

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Displaying Results 1 - 21 of 21
  • Front cover - IEEE Computational Intelligence Magazine

    Page(s): c1
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  • Table of contents - May 2007 Vol 2 No 2

    Page(s): 1
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  • Editor's remarks - We need you...

    Page(s): 2
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  • President's message - Serving your needs...

    Page(s): 3 - 4
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  • Technology review - Biometrics-Technology, Application, Challenge, and Computational Intelligence Solutions

    Page(s): 5 - 25
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  • Application notes - Algorithms for Assessing the Quality of Facial Images

    Page(s): 10 - 17
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1382 KB) |  | HTML iconHTML  

    In this paper, we presented algorithms to assess the quality of facial images affected by factors such as blurriness, lighting conditions, head pose variations, and facial expressions. We developed face recognition prediction functions for images affected by blurriness, lighting conditions, and head pose variations based upon the eigenface technique. We also developed a classifier for images affected by facial expressions to assess their quality for recognition by the eigenface technique. Our experiments using different facial image databases showed that our algorithms are capable of assessing the quality of facial images. These algorithms could be used in a module for facial image quality assessment in a face recognition system. In the future, we will integrate the different measures of image quality to produce a single measure that indicates the overall quality of a face image View full abstract»

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  • Off-line Signature Verification Using an Enhanced Modified Direction Feature with Single and Multi-classifier Approaches

    Page(s): 18 - 25
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2040 KB)  

    The principal objective of this paper was to investigate the efficiency of the enhanced version of the MDF feature extractor for signature verification. Investigations adding new feature values to MDF were performed, assessing the impact on the verification rate of the signatures, using six-fold cross validation. Two different neural classifiers were used and two methodologies for verification were applied. The experiments conducted, whereby MDF was merged with the new features, provided very encouraging results View full abstract»

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  • Computational Intelligence-Based Biometric Technologies

    Page(s): 26 - 36
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2708 KB) |  | HTML iconHTML  

    Computational intelligence (CI) technologies are robust, can be successfully applied to complex problems, are efficiently adaptive, and usually have a parallel computational architecture. For those reasons they have been proved to be effective and efficient in bio-metric feature extraction and biometric matching tasks, sometimes used in combination with traditional methods. In this article, we briefly survey two kinds of major applications of CI in biometric technologies, CI-based feature extraction and CI-based biometric matching. Varieties of evolutionary computation and neural networks techniques have been successfully applied to biometric data representation and dimensionality reduction. CI-based methods, including neural network and fuzzy technologies, have also been extensively investigated for biometric matching. CI-based biometric technologies are powerful when used in the representation and recognition of incomplete biometric data, discriminative feature extraction, biometric matching, and online template updating, and promise to have an important role in the future development of biometric technologies View full abstract»

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  • A pso-based mobile robot for odor source localization in dynamic advection-diffusion with obstacles environment: theory, simulation and measurement

    Page(s): 37 - 51
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2783 KB) |  | HTML iconHTML  

    This paper provides a combination of chemotaxic and anemotaxic modeling, known as odor-gated rheotaxis (OGR), to solve real-world odor source localization problems. Throughout the history of trying to mathematically localize an odor source, two common biometric approaches have been used. The first approach, chemotaxis, describes how particles flow according to local concentration gradients within an odor plume. Chemotaxis is the basis for many algorithms, such as particle swarm optimization (PSO). The second approach is anemotaxis, which measures the direction and velocity of a fluid flow, thus navigating "upstream" within a plume to localize its source. Although both chemotaxic and anemotaxic based algorithms are capable of solving overly-simplified odor localization problems, such as dynamic-bit-matching or moving-parabola problems, neither method by itself is adequate to accurately address real life scenarios. In the real world, odor distribution is multi-peaked due to obstacles in the environment. However, by combining the two approaches within a modified PSO-based algorithm, odors within an obstacle-filled environment can be localized and dynamic advection-diffusion problems can be solved. Thus, robots containing this modified particle swarm optimization algorithm (MPSO) can accurately trace an odor to its source View full abstract»

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  • Robust Speaker Identification and Verification

    Page(s): 52 - 59
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    Acoustic characteristics have played an essential role in biometrics. In this article, we introduce a robust, text-independent speaker identification/verification system. This system is mainly based on a subspace-based enhancement technique and probabilistic support vector machines (SVMs). First, a perceptual filterbank is created from a psycho-acoustic model into which the subspace-based enhancement technique is incorporated. We use the prior SNR of each subband within the perceptual filterbank to decide the estimator's gain to effectively suppress environmental background noises. Then, probabilistic SVMs identify or verify the speaker from the enhanced speech. The superiority of the proposed system has been demonstrated by twenty speaker data taken from AURORA-2 database with added background noises View full abstract»

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  • Developmental Tools - Synthetic Biometrics

    Page(s): 60 - 69
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    In this paper, we draw an analogy to a field which has a great deal in common with computer vision. This field is that of biometric technologies, which attempt to generate computer models of humans' appearance or behavior with a view to reliable personal identification. Computer graphics, in its turn, can be likened to biometric synthesis - rendering biometric phenomena from their corresponding computer models. For example, we could generate a synthetic face from its corresponding computer model. Such a model could include muscular dynamics to model the full gamut of human emotions conveyed by facial expressions View full abstract»

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  • Evolutionary Robotics: From Algorithms to Implementations (Wang, L. et al.; 2006) [Book Review]

    Page(s): 70 - 71
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  • Society Briefs - Bioinformatics and Bioengineering Technical Committee

    Page(s): 72 - 73
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  • Computational Finance and Economics Technical Committee

    Page(s): 73
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  • Data Mining Technical Committee

    Page(s): 74
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  • Games Technical Committee

    Page(s): 74 - 76
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  • Intelligent Systems Applications Technical Committee

    Page(s): 76 - 79
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  • Newly Elected AdCom Members

    Page(s): 79 - 81
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  • Family Corner - Chile Chapter

    Page(s): 82 - 84
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  • Call for Papers - WCCI 2008

    Page(s): 83
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  • Conference Calendar

    Page(s): 86
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Aims & Scope

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications, in keeping with the Field of Interest of the IEEE Computational Intelligence Society (IEEE/CIS). 

 

Full Aims & Scope