Ciência da Computação
URI permanente desta comunidadehttps://repositorio.fei.edu.br/handle/FEI/342
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2 resultados
Resultados da Pesquisa
- Synthesizing 3D face shapes using tensor-based multivariate statistical discriminant methods(2011-11-14) MONOI, J.-L.; Plinio Thomaz Aquino Junior; GILLIES, D. F.We have implemented methods to reconstruct and model 3D face shapes and to synthesize facial expressions from a set of real human 3D face surface maps. The method employed tensor-based statistical shape modelling and statistical discriminant modelling methods. In the statistical shape modelling approach, new face shapes are created by moving the surface points along the appropriate expressive direction in the training set space. In the statistical discriminant model, new face shapes, such as facial expressions, can be synthesized by moving the surface points along the most discriminant direction found from the classes of expressions in the training set. The advantage of the tensor-based statistical discriminant analysis method is that face shapes of varying degrees can be generated from a small number of examples available in the 3D face shape datasets. The results of the reconstructions and synthesis of three-dimensional faces are illustrated in the paper. © 2011 Springer-Verlag.
- Nonlinear discriminant principal component analysis for image classification and reconstruction(2018-12-13) FILISBINO, T.; GIRALDI, G.; Plinio Thomaz Aquino Junior© 2018 IEEE.In this paper we present a nonlinear version of the discriminant principal component analysis, named NDPCA, that is based on kernel support vector machines (KSVM) and the AdaBoost technique. Specifically, the problem of ranking principal components, computed from two-class databases, is addressed by applying the AdaBoost procedure in a nested loop: Each iteration of the inner loop boosts weak classifiers to a moderate one while the outer loop combines the moderate classifiers to build the global discriminant vector. In the proposed NDPCA, each weak learner is a linear classifier computed through a separating hyperplane defined by a KSVM decision boundary in the PCA space. We compare the proposed methodology with counterpart ones using facial expressions of the Radboud and Jaffe image databases. Our experimental results have shown that NDPCA outperforms the PCA in classification tasks. Also, it is competitive if compared with counterpart techniques given also suitable results for reconstruction.