Two-Dimensional Parameter Principal Component Analysis for Face Recognition

Qi Rong Zhang

doi:10.4028/www.scientific.net/AMR.971-973.1838

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 971-973Two-Dimensional Parameter Principal Component...

Two-Dimensional Parameter Principal Component Analysis for Face Recognition

Abstract:

In this paper, we propose a new face recognition approach for image feature extraction named two-dimensional parameter principal component analysis (2DPPCA). Two-dimensional principal component analysis (2DPCA) is widely used in face recognition. We further study on the basis of 2DPCA. This proposed method is to add a parameter to images samples matrix in the image covariance matrix. Extensive experiments are performed on FERET face database and CMU PIE face database. The 2DPPCA method achieves better face recognition performance than PCA, 2DPCA, especially on the CMU PIE face database.

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Periodical:

Advanced Materials Research (Volumes 971-973)

Pages:

1838-1842

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.971-973.1838

Citation:

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Online since:

June 2014

Authors:

Qi Rong Zhang*

Keywords:

Face Recognition, Two-Dimensional Parameter Principal Component Analysis, Two-Dimensional Principal Component Analysis

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* - Corresponding Author

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