J. Weng, and D.L. Swets, "Face Recognition", Biometrics: Personal Identification in Networked Society (A. Jain, R. Bolle, and S. Pankanti, Eds. ), pp. 67--86, Boston, MA: Kluwer Academic, 1999. Home/Search Document Details and Download
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A Nonparametric Statistical Comparison of Principal.. - Beveridge, She.. (2001) (2 citations) (Correct)
....our study. Stepping back from face recognition, characterizing the performance of computer vision algorithms has been an ongoing concern [7, 9] and more is certainly being done in this area each year. In comparison, however, far more is written each year about new and different algorithms. See [14, 15] for recent surveys of face recognition algorithms. Thus, while the literature on algorithms is vast, little has been written about using modern statistical methods [2] to measure uncertainty in performance measures. http: www.cs.colostate.edu evalfacerec One notable exception is the work by ....
J. J. Weng and D. Swets. Face recognition. In Biometrics: Personal Identification in Networked Society. Kluwer Academic Publishers, 1999.
Frontal Face Authentication Using Discriminating Grids.. - Kotropoulos, Tefas.. (1999) (1 citation) (Correct)
....for the following reasons: universality, collectability and acceptability [1] In the following, a brief overview of related previous work is given, and the objectives of our work are outlined. A. Previous work A comprehensive survey of human and machine recognition techniques can be found in [2, 3]. There are several approaches in developing face recognition systems. For example, one approach employs linear projections of face images (treated as 1 D vectors) using either principal component analysis (PCA) 4] or linear discriminant analysis (LDA) 5, 6, 7] PCA and LDA are parametric ....
....feature vector at the l th node is given by j(x t l ) V k Theta P(x l ) Gamma j(x t l ) Gamma m l Delta Gamma m kl . The row vectors v ik , i = 1; 2; d, of V k weigh the most expressive features j Gamma m kl automatically according to their discriminatory power [3]. If a component corresponds to pure random noise, its contribution in the subspace defined by v ik , i = 1; 2; d will be approximately zero. This is not the case with the subspace defined by e 1 , e p , where the contribution of such a noisy component will be roughly proportional ....
J. Weng, and D.L. Swets, "Face Recognition," in Biometrics: Personal Identification in Networked Society (A. Jain, R. Bolle, and S. Pankanti, Eds. ), pp. 67--86, Boston, MA: Kluwer Academic, 1999.
A Statistical Measure for Human Face Symmetry and its.. - Bohus, Fasnacht, Karklin (2000) (Correct)
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J. Weng, and D.L. Swets, "Face Recognition", Biometrics: Personal Identification in Networked Society (A. Jain, R. Bolle, and S. Pankanti, Eds. ), pp. 67--86, Boston, MA: Kluwer Academic, 1999.
Nearest Manifold Approach for Face Recognition - Zhang, Li, Wang (2004) (Correct)
No context found.
J. Weng, and D.L. Swets, "Face Recognition," in Biometrics: Personal Identification in Networked Society (A. Jain, R. Bolle, and S. Pankanti, Eds. ), pp. 67--86, Boston, MA: Kluwer Academic, 1999.
Nearest Manifold Approach for Face Recognition - Zhang, Li, Wang (2004) (Correct)
No context found.
J. Weng, and D.L. Swets, "Face Recognition," in Biometrics: Personal Identification in Networked Society (A. Jain, R. Bolle, and S. Pankanti, Eds. ), pp. 67--86, Boston, MA: Kluwer Academic, 1999.
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