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 via detailing the hot advances and rising theories, equipment, and purposes of biometric platforms in a number of infrastructures. Edited through a panel of specialists, it presents entire insurance of:
- Multilinear discriminant research for biometric sign reputation
- Biometric identification 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 reputation
- Geometrical and statistical types for video-based face authentication
- Near-infrared and 3D face attractiveness
- Recognition in keeping with fingerprints and 3D hand geometry
- Iris reputation and ECG-based biometrics
- Online signature-based authentication
- Identification in keeping with gait
- Information conception methods to biometrics
- Biologically encouraged tools and biometric encryption
- Biometrics in keeping with electroencephalography and event-related potentials
Biometrics: concept, tools, and purposes is an integral source for researchers, defense specialists, policymakers, engineers, and graduate scholars.
Read or Download Biometrics: Theory, methods, and applications PDF
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Extra resources for Biometrics: Theory, methods, and applications
Frangi, J. Yang, D. Zhang, and Z. Jin, KPCA plus LDA: A complete kernel ﬁsher discriminant framework for feature extraction and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 27(2):230–244, 2005. 58. G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan, Learning the kernel matrix with semideﬁnite programming, J. Mach. Learning Res. 5:27–72, 2004. 59. G. Fung, M. Dundar, J. Bi, and B. Rao, A fast iterative algorithm for Fisher discriminant using heterogeneous kernels, in Proceedings of the Twenty-First International Conference on Machine Learning, 2004.
A linear projection takes a vector x ∈ RI and projects it to y ∈ RP using a projection matrix U ∈ RI×P : y = UT x. 4) In typical pattern recognition applications, P I. Therefore, linear projection is a vector-to-vector projection (VVP) and it requires the vectorization of an input before projection. 4a illustrates the VVP of a tensor object A. The classical LDA algorithm employs VVP. 2 Tensor-to-Tensor Projection (TTP) Besides the traditional VVP, we can also project a tensor to another tensor (of the same order), which is named as tensor-to-tensor projection (TTP) in this chapter.
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.