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Patch-Based Principal Component Analysis for Face Recognition
Joint Authors
Jiang, Tai-Xiang
Huang, Ting-Zhu
Zhao, Xi-le
Ma, Tian-Hui
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-11
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition.
Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows.
But the local spatial information is not utilized or not fully utilized in these methods.
We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses.
To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter.
By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction.
Finally, we use the nearest neighbor classifier.
Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA.
Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.
American Psychological Association (APA)
Jiang, Tai-Xiang& Huang, Ting-Zhu& Zhao, Xi-le& Ma, Tian-Hui. 2017. Patch-Based Principal Component Analysis for Face Recognition. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1141003
Modern Language Association (MLA)
Jiang, Tai-Xiang…[et al.]. Patch-Based Principal Component Analysis for Face Recognition. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1141003
American Medical Association (AMA)
Jiang, Tai-Xiang& Huang, Ting-Zhu& Zhao, Xi-le& Ma, Tian-Hui. Patch-Based Principal Component Analysis for Face Recognition. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1141003
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141003