<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Because face images are naturally two-dimensional data, there have been several 2D feature extraction methods to deal with facial images while there are few 2D effective classifiers. Meanwhile, there is an increasing interest in the multilinear subspace analysis and many methods have been proposed to operate directly on these tensorial data during the past several years. One of these popular unsupervised multilinear algorithms is Multilinear Principal Component Analysis (MPCA) while another of the supervised multilinear algorithm is Multilinear Discriminant Analysis (MDA). Then a MPCA+MDA method has been introduced to deal with the tensorial signal. However, due to the no convergence of MDA, it is difficult for MPCA+MDA to obtain a precise result. Hence, to overcome this limitation, a new MPCA plus General Tensor Discriminant Analysis (GTDA) solution with well convergence is presented for tensorial face images feature extraction in this paper. Several experiments are carried out to evaluate the performance of MPCA+GTDA on different databases and the results show that this method has the potential to achieve comparative effect as MPCA+MDA.</p></abstract>

Because face images are naturally two-dimensional data, there have been several 2D feature extraction methods to deal with facial images while there are few 2D effective classifiers. Meanwhile, there is an increasing interest in the multilinear subspace analysis and many methods have been proposed to operate directly on these tensorial data during the past several years. One of these popular unsupervised multilinear algorithms is Multilinear Principal Component Analysis (MPCA) while another of the supervised multilinear algorithm is Multilinear Discriminant Analysis (MDA). Then a MPCA+MDA method has been introduced to deal with the tensorial signal. However, due to the no convergence of MDA, it is difficult for MPCA+MDA to obtain a precise result. Hence, to overcome this limitation, a new MPCA plus General Tensor Discriminant Analysis (GTDA) solution with well convergence is presented for tensorial face images feature extraction in this paper. Several experiments are carried out to evaluate the performance of MPCA+GTDA on different databases and the results show that this method has the potential to achieve comparative effect as MPCA+MDA.

A Report on Multilinear PCA Plus GTDA to Deal With Face Image

School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450045, China

School of Information Engineering, Yulin University, Yulin, 719000, China

Mathematics and Computational Science College, Guilin University of Electronic Technology, Guilin, 541000, China

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"A Report on Multilinear PCA Plus GTDA to Deal With Face Image"}

Because face images are naturally two-dimensional data, there have been several 2D feature extraction methods to deal with facial images while there are few 2D...

A Report on Multilinear PCA Plus GTDA to Deal With Face Image

Publicado en línea: 09 abr 2016

Páginas: 146 - 157

DOI: https://doi.org/10.1515/cait-2016-0012

Palabras clave
Feature extraction, tensor objects, face recognition, multilinear principal component analysis, general tensor discriminant analysis

© 2016 Fan Zhang et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Report on Multilinear PCA Plus GTDA to Deal With Face Image

Publicado en línea: 09 abr 2016

Páginas: 146 - 157

DOI: https://doi.org/10.1515/cait-2016-0012

Palabras claveFeature extraction, tensor objects, face recognition, multilinear principal component analysis, general tensor discriminant analysis

© 2016 Fan Zhang et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
Feature extraction, tensor objects, face recognition, multilinear principal component analysis, general tensor discriminant analysis