0 citations0 references

Two-dimensional pca: a new approach to appearance-based face representation and recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence2004Vol. 26(1), pp. 131–137

Citations Over TimeTop 1% of 2004 papers

Jian Yang, David Zhang, Alejandro F. Frangi, Jingyu Yang

Abstract

In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.

Related Papers

Methodological analysis of Principal Component Analysis (PCA) method(2013)
→ Effect Of Eigenfaces Level On The Face Recognition Rate Using Principal Component Analysis(2015)3 cited
Face recognition using Eigenface/PCA / Muhammad Fadzli Ahmad Jalalulmahalli(2005)