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.
Read Online or Download Biometrics: Theory, methods, and applications PDF
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Extra resources for Biometrics: Theory, methods, and applications
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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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