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An MPCA/LDA Based Dimensionality Reduction Algorithm for Face Recognition

Huang, Jun, Su, Kehua, El-Den, Jamal, Hu, Tao and Li, Junlong (2014). An MPCA/LDA Based Dimensionality Reduction Algorithm for Face Recognition. Mathematical Problems in Engineering,2014.

Document type: Journal Article
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IRMA ID 75039815xPUB697
Title An MPCA/LDA Based Dimensionality Reduction Algorithm for Face Recognition
Author Huang, Jun
Su, Kehua
El-Den, Jamal
Hu, Tao
Li, Junlong
Journal Name Mathematical Problems in Engineering
Publication Date 2014
Volume Number 2014
ISSN 1024-123X   (check CDU catalogue  open catalogue search in new window)
Total Pages 12
Place of Publication United States of America
Publisher Hindawi Publishing Corporation
HERDC Category C1 - Journal Article (DIISR)
Abstract We proposed a face recognition algorithm based on both the multilinear principal component analysis (MPCA) and linear discriminant analysis (LDA). Compared with current traditional existing face recognition methods, our approach treats face images as multidimensional tensor in order to find the optimal tensor subspace for accomplishing dimension reduction. The LDA is used to project samples to a new discriminant feature space, while the K nearest neighbor (KNN) is adopted for sample set classification. The results of our study and the developed algorithm are validated with face databases ORL, FERET, and YALE and compared with PCA, MPCA, and PCA + LDA methods, which demonstrates an improvement in face recognition accuracy.
DOI http://dx.doi.org/10.1155/2014/393265   (check subscription with CDU E-Gateway service for CDU Staff and Students  check subscription with CDU E-Gateway in new window)
Additional Notes This is an Open Access article distributed under the terms of the Creative Commons Attribution License 3.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Description for Link Link to CC Attribution 3.0 License
URL https://creativecommons.org/licenses/by/3.0/au


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