An MPCALDA Based Dimensionality Reduction Algorithm for Face Recognition
Joint Authors
Hu, Tao
Su, Kehua
Huang, Jun
El-Den, Jamal
Li, Junlong
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-31
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Huang, Jun& Su, Kehua& El-Den, Jamal& Hu, Tao& Li, Junlong. 2014. An MPCALDA Based Dimensionality Reduction Algorithm for Face Recognition. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1044221
Modern Language Association (MLA)
Huang, Jun…[et al.]. An MPCALDA Based Dimensionality Reduction Algorithm for Face Recognition. Mathematical Problems in Engineering No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1044221
American Medical Association (AMA)
Huang, Jun& Su, Kehua& El-Den, Jamal& Hu, Tao& Li, Junlong. An MPCALDA Based Dimensionality Reduction Algorithm for Face Recognition. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1044221
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1044221