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Face Representation And Recognition Using Two-Dimensional PCA

Author(s): K.Shilpa | Syed Musthak Ahmed | A.VenkataRamana

Journal: International Journal of Computer Technology and Applications
ISSN 2229-6093

Volume: 03;
Issue: 01;
Start page: 80;
Date: 2012;
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Keywords: Principal Component Analysis (PCA) | Eigenfaces | feature extraction | image representation | face recognition

In this paper, two-dimensionalprincipal component analysis (2DPCA) is used forimage representation and recognition. Compared to1D PCA, 2DPCA is based on 2D image matricesrather than 1D vectors so the image matrix does notneed to be transformed into a vector prior to featureextraction. Instead, an image covariance matrix isconstructed directly using the original imagematrices, and its eigenvectors are derived for imagefeature extraction. In order to test the approach, wehave used ORL face database images. Therecognition rate across all trials was higher using2DPCA than PCA. The experimental results showsthat this approach of extraction of image features iscomputationally more efficient using 2DPCA thanPCA. It is also observed from the results that therecognition rate is high.

Tango Jona
Tangokurs Rapperswil-Jona

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