By N. V. Boulgouris, Konstantinos N. Plataniotis, Evangelia Micheli-Tzanakou
An in-depth exam of the leading edge of biometrics
This publication fills a spot within the literature by way of detailing the hot advances and rising theories, tools, and functions of biometric structures in quite a few infrastructures. Edited through a panel of specialists, it offers entire assurance of:
- Multilinear discriminant research for biometric sign popularity
- Biometric id authentication innovations 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 popularity
- Recognition according to fingerprints and 3D hand geometry
- Iris popularity and ECG-based biometrics
- Online signature-based authentication
- Identification according to gait
- Information conception techniques to biometrics
- Biologically encouraged tools and biometric encryption
- Biometrics according to electroencephalography and event-related potentials
Biometrics: concept, tools, and functions is an necessary source for researchers, protection specialists, policymakers, engineers, and graduate scholars.
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Additional info for Biometrics: Theory, methods, and applications
14. J. B. Tenenbaum, V. d. Silva, and J. C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science 290(5500):279–294, 2000. 15. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, New York, 2006. 16. R. O. Duda, P. E. Hart, and D. Stork, Pattern Classiﬁcation, John Wiley & Sons, New York, 2000. 17. T. Hastie, R. H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York, 2001. 18. A. M. Martinez and M.
6 A random subset with L (= 5, 10, 20, 30) samples per subject was taken with labels to form the training set, and the rest of the database was considered to be the testing set. For each given L, the results averaged over 20 random splits7 are reported in this chapter. The nearest-neighbor classiﬁer with the Euclidean distance measure was employed in classiﬁcation for simplicity. The MLDA-TTP variants (DATER and GTDA) produce features in tensor representation, which cannot be handled directly by the selected classiﬁer.
That is, for any x1 , . . , xn ∈ X , the kernel Gram matrix K ∈ IRn×n , deﬁned by Kij = κ(xi , xj ), is positive semideﬁnite. Any kernel function κ implicitly maps the input set X to a high-dimensional (possibly inﬁnite) Hilbert space Hκ equipped with the inner product (·, ·)Hκ through a mapping φκ from X to Hκ : κ(x, z) = (φκ (x), φκ (z))Hκ. 33) j=1 x∈Xj φ where cj is the centroid of the jth class and cφ is the global centroid in the feature space. Similar to the linear case, the transformation G of KDA can be computed by solving the following optimization problem: G = arg max trace G φ G T St G + φ G T Sb G .