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 effective classifiers. Meanwhile, there is an increasing interest in the multilinear subspace analysis and many methods have been proposed t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2016-03-01
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| Series: | Cybernetics and Information Technologies |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/cait-2016-0012 |
| _version_ | 1818393107160367104 |
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| author | Zhang Fan Wang Xiaoping Sun Ke |
| author_facet | Zhang Fan Wang Xiaoping Sun Ke |
| author_sort | Zhang Fan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T05:40:03Z |
| format | Article |
| id | doaj.art-884ac8d15b6a47a0bc296baaf88264f6 |
| institution | Directory Open Access Journal |
| issn | 1314-4081 |
| language | English |
| last_indexed | 2024-12-14T05:40:03Z |
| publishDate | 2016-03-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Cybernetics and Information Technologies |
| spelling | doaj.art-884ac8d15b6a47a0bc296baaf88264f62022-12-21T23:15:02ZengSciendoCybernetics and Information Technologies1314-40812016-03-0116114615710.1515/cait-2016-0012A Report on Multilinear PCA Plus GTDA to Deal With Face ImageZhang Fan0Wang Xiaoping1Sun Ke2School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450045, ChinaSchool of Information Engineering, Yulin University, Yulin, 719000, ChinaMathematics and Computational Science College, Guilin University of Electronic Technology, Guilin, 541000, ChinaBecause 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.https://doi.org/10.1515/cait-2016-0012feature extractiontensor objectsface recognitionmultilinear principal component analysisgeneral tensor discriminant analysis |
| spellingShingle | Zhang Fan Wang Xiaoping Sun Ke A Report on Multilinear PCA Plus GTDA to Deal With Face Image Cybernetics and Information Technologies feature extraction tensor objects face recognition multilinear principal component analysis general tensor discriminant analysis |
| title | A Report on Multilinear PCA Plus GTDA to Deal With Face Image |
| title_full | A Report on Multilinear PCA Plus GTDA to Deal With Face Image |
| title_fullStr | A Report on Multilinear PCA Plus GTDA to Deal With Face Image |
| title_full_unstemmed | A Report on Multilinear PCA Plus GTDA to Deal With Face Image |
| title_short | A Report on Multilinear PCA Plus GTDA to Deal With Face Image |
| title_sort | report on multilinear pca plus gtda to deal with face image |
| topic | feature extraction tensor objects face recognition multilinear principal component analysis general tensor discriminant analysis |
| url | https://doi.org/10.1515/cait-2016-0012 |
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