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 leading edge of biometrics
This booklet fills a spot within the literature through detailing the hot advances and rising theories, equipment, and functions of biometric structures in a number of infrastructures. Edited through a panel of specialists, it offers complete insurance of:
- Multilinear discriminant research for biometric sign attractiveness
- Biometric id authentication options according to neural networks
- Multimodal biometrics and layout of classifiers for biometric fusion
- Feature choice and facial getting older modeling for face attractiveness
- Geometrical and statistical versions for video-based face authentication
- Near-infrared and 3D face popularity
- Recognition in keeping with fingerprints and 3D hand geometry
- Iris attractiveness and ECG-based biometrics
- Online signature-based authentication
- Identification in accordance with gait
- Information idea methods to biometrics
- Biologically encouraged equipment and biometric encryption
- Biometrics in keeping with electroencephalography and event-related potentials
Biometrics: concept, equipment, and functions is an integral source for researchers, protection specialists, policymakers, engineers, and graduate scholars.
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Extra resources for Biometrics: Theory, methods, and applications
K. Jain and S. Z. Li, Handbook of Face Recognition, Springer-Verlag, New York, 2005. 3. A. K. Jain, A. A. Ross, and S. Prabhakar, An introduction to biometric recognition, IEEE Trans. Circuits Syt. Video Technol. 14(1):4–20, 2004. 4. R. E. Bellman, Adaptive Control Processes: A Guided Tour, Princeton University Press, Princeton, NJ, 1961. 5. D. Donoho, High-dimensional data analysis: The curses and blessings of dimensionality, in American Mathematical Society Lecture—Math Challenges of the 21st Century, August 2000.
Golub and C. F. Van Loan, Matrix Computations, 3rd edition, The Johns Hopkins University Press, Baltimore, 1996. 28. W. Zhao, R. Chellappa, and P. Phillips, Subspace linear discriminant analysis for face recognition, Technical Report CAR-TR-914, Center for Automation Research, University of Maryland, 1999. 29. V. N. Vapnik. Statistical Learning Theory, John Wiley & Sons, New York, 1998. 30. L. Duchene and S. Leclerq, An optimal transformation for discriminant and principal component analysis, IEEE Trans.
Up(N) T p = 1, . . , P, , T up(n) , n = 1, . . 8) . Thus, this TVP is written as T (n) y = X ×N n=1 up , n = 1, . . 9) where the pth component of y is obtained from the pth EMP as T T T y(p) = X ×1 up(1) ×2 up(2) · · · ×N up(N) . 4c shows the TVP of a tensor object A to a vector of size P × 1. A number of recent multilinear algorithms [27, 28, 30, 35]5 have been proposed with the objective of solving such a TVP. 4 MLDA-TTP The multilinear extension of the LDA using the TTP is named MLDA-TTP hereafter.