Biometric Image Discrimination Technologies by David Zhang

By David Zhang

Biometric photograph Discrimination applied sciences addresses hugely appropriate concerns to many basic matters of either researchers and practitioners of biometric snapshot discrimination (BID) in biometric functions. This ebook describes the elemental ideas worthy for an exceptional realizing of BID and solutions a few vital introductory questions about BID.Biometric photograph Discrimination applied sciences covers the theories that are the rules of simple BID applied sciences, whereas constructing new algorithms that are established to be better in biometrics authentication. This publication will help scholars new to the sector and also will be valuable to senior researchers during this region.

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21) In particular, because eT St e = λ eTe = λ, it follows that to maximize eT S te, we want to select the eigenvector corresponding to the largest eigenvalue of the scatter matrix. In other words, to find the best one-dimensional projection of the data (best in the leastsum-of-squared-sense), we project the data onto a line through the sample mean in the direction of the eigenvector of the scatter matrix having the largest eigenvalue. This result can be readily extended from a one-dimensional projection to a d’ dimensional projection.

22 Zhang, Jing & Yang eigenfaces correspond to the eigenvectors associated with the dominant eigenvalues of the face covariance matrix. The eigenfaces define a feature space, or “face space,” which drastically reduces the dimensionality of the original space, and face detection and identification are carried out in the reduced space (Zhang, 1997). Since then, PCA has been widely investigated and become one of the most successful approaches in face recognition (Pentland, 2000; Grudin, 2000; Cottrell & Fleming, 1990; Valentin, Abdi, O’Toole, & Cottrell, 1994).

Wong, M. (2003). On-line palmprint identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(9), 10411050. K. (2002b). A novel face recognition system using hybrid neural and dual eigenfaces methods. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 32(6) 787-793. , & Yang, Y. (1999). Theoretical analysis of illumination in PCA-based vision systems. Pattern Recognition, 32(4), 547-564. , & Weng, J. (1998). Discriminant analysis of principal components for face recognition.

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