Download Biometrics: Theory, methods, and applications by N. V. Boulgouris, Konstantinos N. Plataniotis, Evangelia PDF

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

An in-depth exam of the innovative of biometrics

This e-book fills a spot within the literature by means of detailing the new advances and rising theories, tools, and purposes of biometric structures in various infrastructures. Edited by means of a panel of specialists, it offers accomplished assurance of:

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

Biometrics: idea, equipment, and purposes is an essential source for researchers, protection specialists, policymakers, engineers, and graduate scholars.

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Extra resources for Biometrics: Theory, methods, and applications

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Zhou, O. Bousquet, T. Lal, J. Weston, and B. Sch¨olkopf, Learning with local and global consistency, in Advances in Neural Information Processing Systems, 2003, pp. 321–328. 43. X. Zhu, Z. Ghahramani, and J. Lafferty, Semi-supervised learning using Gaussian fields and harmonic functions, in Proceedings of the 20th International Conference on Machine Learning, 2003, pp. 912–919. 44. J. Chen, J. Ye, and Q. Li, Integrating global and local structures: A least squares framework for dimensionality reduction, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007, pp.

It can be verified that 1 2 n n zi − z j i=1 j=1 where Z = [z1 , . . , zn ]. 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.

31. J. Ye, T. Xiong, Q. Li, R. Janardan, J. Bi, V. Cherkassky, and C. Kambhamettu, Efficient model selection for regularized linear discriminant analysis, in Proceedings of the 15th ACM International Conference on Information and Knowledge Management, 2006, pp. 532–539. 32. H. Cevikalp, M. Neamtu, M. Wilkes, and A. Barkana, Discriminative common vectors for face recognition, IEEE Trans. Pattern Anal. Mach. Intell. 27(1):4–13, 2005. 33. J. Ye and T. Xiong, Computational and theoretical analysis of null space and orthogonal linear discriminant analysis, J.

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