Intelligence Semantics

New PDF release: Biometrics: Theory, methods, and applications

By N. V. Boulgouris, Konstantinos N. Plataniotis, Evangelia Micheli-Tzanakou

ISBN-10: 0470247827

ISBN-13: 9780470247822

An in-depth exam of the innovative of biometrics

This booklet fills a niche within the literature via detailing the hot advances and rising theories, tools, and purposes of biometric structures in a number of infrastructures. Edited by way of a panel of specialists, it presents finished insurance of:

  • Multilinear discriminant research for biometric sign attractiveness
  • Biometric identification authentication ideas in keeping with neural networks
  • Multimodal biometrics and layout of classifiers for biometric fusion
  • Feature choice and facial getting older modeling for face acceptance
  • Geometrical and statistical versions for video-based face authentication
  • Near-infrared and 3D face reputation
  • Recognition in response to fingerprints and 3D hand geometry
  • Iris popularity and ECG-based biometrics
  • Online signature-based authentication
  • Identification in accordance with gait
  • Information thought techniques to biometrics
  • Biologically encouraged tools and biometric encryption
  • Biometrics in line with electroencephalography and event-related potentials

Biometrics: idea, tools, and functions is an critical source for researchers, defense specialists, policymakers, engineers, and graduate scholars.

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New PDF release: Biometrics: Theory, methods, and applications

An in-depth exam of the leading edge of biometrics This ebook fills a spot within the literature by way of detailing the new advances and rising theories, equipment, and purposes of biometric structures in various infrastructures. Edited through a panel of specialists, it presents complete insurance of: Multilinear discriminant research for biometric sign popularity Biometric identification authentication suggestions in response to neural networks Multimodal biometrics and layout of classifiers for biometric fusion characteristic choice and facial getting older modeling for face reputation Geometrical and statistical versions for video-based face authentication Near-infrared and 3D face acceptance acceptance in accordance with fingerprints and 3D hand geometry Iris acceptance and ECG-based biometrics on-line signature-based authentication id in response to gait info idea ways to biometrics Biologically encouraged equipment and biometric encryption Biometrics according to electroencephalography and event-related potentials Biometrics: idea, tools, and purposes is an vital source for researchers, safety specialists, policymakers, engineers, and graduate scholars.

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Extra resources for Biometrics: Theory, methods, and applications

Sample text

Anouar, Generalized discriminant analysis using a kernel approach, Neural Comput. 12(10):2385–2404, 2000. 52. S. Mika, G. R¨atsch, J. Weston, B. Sch¨olkopf, A. -R. M¨uller, Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces, IEEE Trans. Pattern Anal. Mach. Intell. 25(5):623–633, 2003. 53. J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, Face recognition using kernel direct discriminant analysis algorithms, IEEE Trans. Neural Networks 14(1):117– 126, 2003.

H. Park, M. Jeon, and J. B. Rosen, Lower dimensional representation of text data based on centroids and least squares, BIT 43(2):1–22, 2003. 26. P. Howland, M. Jeon, and H. Park, Structure preserving dimension reduction for clustered text data based on the generalized singular value decomposition, SIAM J. Matrix Anal. Appl. 25(1):165–179, 2003. 27. G. H. Golub and C. F. Van Loan, Matrix Computations, 3rd edition, The Johns Hopkins University Press, Baltimore, 1996. 28. W. Zhao, R. Chellappa, and P.

2 A Regularization Framework for Semisupervised LDA In semisupervised LDA, information from unlabeled data is incorporated into the formulation via a regularization term defined as in Eq. 28). 29) where γ ≥ 0 is a tuning parameter and Y3 is the class indicator matrix defined in Eq. 24). Since the Laplacian regularizer in Eq. 29) does not depend on the label information, the unlabeled data can be readily incorporated into the formulation. Thus the locality structures of both labeled and unlabeled data points are captured through the transformation W.

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Biometrics: Theory, methods, and applications by N. V. Boulgouris, Konstantinos N. Plataniotis, Evangelia Micheli-Tzanakou


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